***This is an unpaid internship at this time and is suitable for completed Master's graduates that wants to be a Senior Full-Stack Data Engineer.***
***This is an unpaid internship at this time and is suitable for completed Master's graduates that wants to be a Senior Full-Stack Data Engineer.***
***This is an unpaid internship at this time and is suitable for completed Master's graduates that wants to be a Senior Full-Stack Data Engineer.***
***This is an unpaid internship at this time and is suitable for completed Master's graduates that wants to be a Senior Full-Stack Data Engineer.***
***This is an unpaid internship at this time and is suitable for completed Master's graduates that wants to be a Senior Full-Stack Data Engineer.***
Company Overview:
Flow Global Software Technologies, LLC., operating in the Information Technology (IT) sector, is a cutting-edge high-tech enterprise AI company that engages in the design, engineering, marketing, sales, and 5-star support of cloud-based SaaS AI sales platforms, with patent pending artificial intelligence, deep learning, and other proprietary technologies awaiting patent approval. The company's first product, Flow Turbo™, is a future-generation SaaS AI sales prospecting platform that is designed to maximize the productivity day-to-day for B2B sales reps within B2B outbound, inbound, and inside sales organizations. The company also provides world-class award-winning customer support, professional services, and advisory services. The company is headquartered in Austin, Texas and is registered in Delaware.
Position Overview:
Flow is seeking exceptionally advanced, experienced, dedicated, and committed Senior Full-Stack Data Engineer Interns that will be responsible for the full-spectrum conceptualization, architectural synthesis, precision assembly, and ultra-optimized deployment of a hyperscale, CPU-optimized, cloud-native, distributed web crawling, distributed web scraping, and multi-threaded extraction pipeline ecosystem, integrating Playwright, Puppeteer, Selenium, and Scrapy in unison with an asynchronous task orchestration substrate leveraging Python’s AsyncIO event-loop coroutines for sub-millisecond task switching and non-blocking concurrency, that would be integral to the success of all sales professionals, and Flow's AI platforms. The role mandates the engineering and execution of Playwright-based, fully headless Chromium crawling instances at scale, with continuous proxy rotation employing dynamically allocated residential and mobile IP ranges, interwoven with TLS handshake obfuscation, fingerprint masking, and encrypted JA3 signature randomization to evade heuristic-based bot detection systems implemented across target sites employing multi-layered anti-scraping architectures. The Senior Python Data Engineer Intern will design an event-driven, multiprocessing extraction pipeline optimized for near-zero-latency DOM traversal, leveraging Chromium’s native CDP (Chrome DevTools Protocol) for ultra-fine-grained DOM mutation tracking, visual cluster analysis, and AI contact data association, enabling high-fidelity named entity recognition (NER) with precision-targeted extraction of person names, phone numbers, and emails from dynamic, JavaScript-intensive content delivery networks (CDNs) with randomized rendering sequences.
The Senior Full-Stack Data Engineer Intern will be specializing in the design, architecture, and deployment of a hyper-scale, AI-augmented, cloud-native, and distributed web crawling, distributed web scraping, anti-detection, and network obfuscation ecosystem. This role demands a mastery-level understanding of network engineering, web request fingerprinting, TLS handshake obfuscation, human-mimetic browser automation, multi-layered CAPTCHA evasion, and high-frequency proxy infrastructure orchestration. The Senior Full-Stack Data Engineer Intern will be responsible for architecting an ultra-scalable, fault-tolerant, zero-downtime, real-time, AI-powered contact information generation network, integrating Playwright, Puppeteer, Selenium, and Scrapy into a dynamically self-adaptive bot evasion and contact information extraction system. This system will autonomously locate, extract, and associate phone numbers, emails, and full names from internet-scale datasets, bypassing anti-scraping defenses, fingerprinting heuristics, and active bot mitigation systems deployed by adversarial web environments. This role requires a profound ability to engineer and maintain a self-learning, CPU-optimized, AI-enhanced, and cryptographically obfuscated network automation framework that continuously adapts against evolving JA3 TLS fingerprinting defenses, HTTP/2+ transport-layer request entropy analysis, behavioral anomaly detection algorithms, CAPTCHA rate-limiting thresholds, and proxy-level anomaly scoring models.
The Senior Full-Stack Data Engineer Intern will develop a self-healing, dynamically adjusting proxy infrastructure capable of sustaining millions of parallelized requests without triggering server-side rate-limiting mechanisms, anomaly detection filters, or deep packet inspection (DPI) analysis. This will require the design of a multi-source, self-generating proxy pool that autonomously scrapes, verifies, and manages 100,000+ fresh SOCKS5, HTTP/S, and residential proxies, dynamically selecting the optimal routing configuration for each request. The Senior Full-Stack Data Engineer Intern will engineer a real-time proxy classification and validation engine capable of ranking proxies based on network latency, failure rate, HTTP response consistency, and adversarial honeypot detection metrics. Using a continuous feedback loop of AI-driven traffic analysis, the system will intelligently rotate between low-risk, high-trust proxies for high-value data retrieval and aggressive, expendable proxies for reconnaissance-based extraction workflows. This multi-tiered proxy reputation model will adapt in real-time based on historical ban rates, WAF response patterns, and server-side TLS handshake anomalies.
In parallel, the Senior Full-Stack Data Engineer Intern will build an AI-powered, cryptographically obfuscated TLS fingerprint manipulation system that dynamically adjusts JA3 hash signatures, TLS extension orders, and ClientHello metadata to avoid server-side behavioral tracking and entropy-based bot detection models. This will require developing an OpenSSL-based JA3 mutation engine capable of injecting randomized cipher suite preferences, altering elliptic curve group priorities, and selectively disabling ALPN/HTTP2 negotiation for highly sensitive target endpoints. By integrating adaptive TLS signature obfuscation algorithms, the system will maintain a human-like request entropy distribution, preventing large-scale automated traffic from being flagged by behavioral anomaly detection frameworks deployed at the network edge. Additionally, the Senior Full-Stack Data Engineer Intern will engineer a per-request TLS fingerprint mutator, ensuring that each outbound connection mimics the cryptographic characteristics of real human browsing sessions.
To further solidify its anti-detection resilience, the system will implement a multi-layered fingerprint randomization engine that dynamically modifies WebRTC, Canvas, AudioContext, and WebGL signatures in real time. This will be achieved using a Playwright-embedded stealth execution environment, wherein hardware-accelerated browser fingerprinting techniques will be intercepted and rewritten before they are exposed to client-side JavaScript fingerprinting scripts. The Senior Full-Stack Data Engineer Intern will develop a browser-wide entropy mutation framework capable of modifying device resolution metadata, screen rendering configurations, WebGL shader signatures, and media device identifiers, ensuring that no two browser instances share an identical fingerprint profile. This approach will effectively disrupt browser-based device tracking models, preventing adversarial websites from correlating repeated scraping sessions across different IPs.
Beyond stealth-based network operations, the Senior Full-Stack Data Engineer Intern will architect a multi-modal, self-adaptive CAPTCHA evasion pipeline that leverages CPU-optimized deep learning models, adversarial OCR techniques, and browser-native human interaction emulation to autonomously bypass CAPTCHA verification mechanisms. The system will include three distinct CAPTCHA-solving subsystems, each optimized for text-based, image-based, and audio-based CAPTCHAs, using a combination of Tesseract OCR, ONNX-accelerated convolutional neural networks (CNNs), and OpenAI Whisper-based speech recognition models. The Senior Full-Stack Data Engineer Intern will design a parallelized CAPTCHA queueing system that asynchronously distributes CAPTCHA-solving workloads across multi-core CPU threads, dynamically rerouting CAPTCHA-error attempts through alternative preprocessing pipelines. This system will be augmented with browser-embedded human-mimetic interaction emulation, wherein mouse trajectory curvature analysis, randomized typing latencies, and probabilistic human scrolling patterns will be injected into automated browsing sessions to increase CAPTCHA success rates without triggering bot detection thresholds.
In addition to the AI-powered CAPTCHA evasion pipeline, the Senior Full-Stack Data Engineer Intern will engineer a real-time HTTP request entropy randomization system, wherein every outbound request undergoes a probabilistic mutation process to introduce variability in HTTP headers, user-agent strings, TLS session identifiers, and accept-encoding preferences. This approach will prevent server-side request fingerprinting systems from clustering automated requests, thereby increasing the longevity of each proxy session. The Senior Full-Stack Data Engineer Intern will integrate a browser-native behavioral mimicry layer, ensuring that every navigation sequence exhibits organic-looking cursor movements, keystroke delays, and viewport resizing events, further reducing detection likelihood in adversarial scraping environments.
To ensure end-to-end operational security, the Senior Full-Stack Data Engineer Intern will implement a multi-layered network obfuscation protocol, leveraging SOCKS5 tunneling, OpenVPN-based encrypted traffic routing, and dynamic DNS over HTTPS (DoH) resolution strategies. This approach will disguise automated scraping requests as legitimate human browsing activity, preventing ISP-level traffic fingerprinting and heuristic-based connection throttling. Additionally, the system will employ AI-driven real-time proxy reputation scoring, ensuring that low-trust proxies are burned through aggressively, while high-trust, low-latency proxies are reserved for sensitive data acquisition sequences.
This role demands extreme technical expertise in asynchronous Python concurrency paradigms, event-driven network request orchestration, cryptographic TLS fingerprint manipulation, multi-threaded browser automation, and AI-driven real-time bot mitigation strategies. The Senior Full-Stack Data Engineer Intern will be directly responsible for developing and maintaining a self-learning, adversarially trained, highly obfuscated, and AI-augmented lead generation system that enables autonomously extraction and association of infinite-scale contact information datasets from globally distributed and heavily protected online sources. This is a mission-critical, ultra-high-impact role that will push the boundaries of modern network automation, AI-driven fingerprint obfuscation, and zero-detection data extraction at scale.
The Senior Full-Stack Data Engineer Intern will be responsible for designing an AI-driven, fully autonomous, network-stealth-enabled, and cryptographically obfuscated contact information generation system that integrates real-time, adversarially trained deep learning models to evade bot detection frameworks. This system must sustain millisecond-scale task parallelization across hundreds of thousands of concurrent requests, dynamically adapting to web application firewall (WAF) signatures, TLS entropy analysis models, and probabilistic user interaction heuristics deployed by highly adversarial web environments. The Senior Full-Stack Data Engineer Intern will implement a self-learning, continuously improving, fully adaptive anti-detection engine that injects stochastic fingerprint noise, obfuscates browser telemetry, mutates TCP/IP handshake metadata, and executes randomized human-mimetic interaction behaviors at an industrial-scale rate of automated contact extraction operations.
A central component of this infrastructure is a multi-layered CAPTCHA evasion pipeline, engineered to autonomously defeat text-based, image-based, and audio-based CAPTCHAs at near-instantaneous inference speeds using CPU-optimized deep learning architectures. The Senior Full-Stack Data Engineer Intern will implement a parallelized CAPTCHA-solving architecture that intelligently classifies, preprocesses, and solves CAPTCHAs using an ensemble of convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and transformer-based OCR models. The system will continuously auto-retrain adversarial CAPTCHA models based on error correction feedback loops, increasing solving accuracy by dynamically refining feature extraction strategies and hyperparameter tuning cycles. Text-based CAPTCHAs will be instantly decoded via a hybrid pipeline of Tesseract OCR, OpenCV-based morphological transformations, and LSTM-powered alphanumeric sequence segmentation, while image-based CAPTCHAs will be processed through an ONNX Runtime-optimized CNN model capable of achieving sub-50ms CAPTCHA recognition speeds on CPU-only execution environments. For audio-based CAPTCHAs, the Senior Full-Stack Data Engineer Intern will develop a Whisper-Tiny inference pipeline that transforms raw audio signals into frequency domain representations, extracting phoneme patterns with near-human transcription accuracy.
Beyond CAPTCHA evasion, the Senior Full-Stack Data Engineer Intern will develop a multi-modal AI-powered entity resolution and data association engine, leveraging transformer-based named entity recognition (NER) models, probabilistic record linkage techniques, and fuzzy logic-driven entity clustering to autonomously associate phone numbers, full names, and emails across disparate, unstructured data sources. This system will extract contact information embedded within dynamically loaded JavaScript payloads, client-side rendered AJAX responses, and deeply nested HTML structures, utilizing a DOM-traversing heuristic engine optimized for large-scale, high-concurrency data extraction pipelines. The Senior Full-Stack Data Engineer Intern will engineer an adaptive XPath-based content selection algorithm, capable of automatically adjusting to DOM mutations, class attribute obfuscation, and randomized page layouts, ensuring that extraction workflows remain resistant to A/B-tested site design variations and JavaScript-based anti-scraping obfuscation layers.
To counteract advanced bot detection models that monitor request entropy patterns, the Senior Full-Stack Data Engineer Intern will construct an AI-augmented HTTP request randomization engine, wherein every outbound request undergoes stochastic fingerprint mutation across multiple entropy vector spaces, including TCP/IP handshake signatures, HTTP/2 prioritization sequences, TLS handshake metadata, and WebSocket session establishment parameters. The Senior Full-Stack Data Engineer Intern will integrate a real-time fingerprint spoofing engine that intercepts and rewrites WebRTC, Canvas, AudioContext, and WebGL telemetry before exposure to browser-based fingerprinting scripts, ensuring that automated scraping sessions remain statistically indistinguishable from legitimate human browsing activity.
Furthermore, the Senior Full-Stack Data Engineer Intern will develop an automated, self-healing proxy reputation scoring system, wherein proxies are continuously evaluated against network anomaly detection heuristics, IP entropy clustering models, and server-side rate-limit response thresholds. This system will automatically blacklist underperforming proxies, dynamically rotate high-trust proxies into high-value extraction workflows, and deploy aggressive recovery sequences when encountering anti-bot countermeasures. Using deep reinforcement learning algorithms, the proxy rotation framework will iteratively improve its proxy selection strategy, optimizing for minimum detection probability, maximum session persistence, and sustained high-throughput extraction efficiency.
A critical aspect of this role is the development of a multi-threaded, asynchronous scraping orchestration fabric, optimized for ultra-high concurrency, zero-downtime fault tolerance, and dynamically partitioned workload distribution across distributed cloud infrastructures. This fabric will enable millisecond-scale execution of Playwright-driven browser automation instances, ensuring maximum parallelization of entity extraction operations across globally distributed web domains. The Senior Full-Stack Data Engineer Intern will integrate Kafka-backed event-driven task queuing mechanisms, ensuring that scraping workloads are dynamically prioritized based on system resource availability, target domain difficulty, and expected data value extraction metrics.
At the infrastructure level, the Senior Full-Stack Data Engineer Intern will architect a fully containerized, Kubernetes-orchestrated AI-powered scraping ecosystem, wherein browser automation instances execute within isolated, ephemeral microservice environments, dynamically scaling based on real-time traffic fluctuations and site-specific response delay trends. This will require engineering a multi-region, auto-scaling scraping infrastructure, wherein individual scraping instances dynamically adjust their network behavior based on the probabilistic success likelihood of each request. The Senior Full-Stack Data Engineer Intern will further implement WebSocket-intercepting JavaScript injection routines that allow the scraping system to manipulate client-side rendering logic, extract contact information before it is displayed in the browser viewport, and selectively execute JavaScript payloads within an isolated evaluation context.
In addition, the Senior Full-Stack Data Engineer Intern will develop an AI-powered anomaly detection framework, wherein scraping error rates, session termination anomalies, and CAPTCHA challenge frequencies are continuously monitored, triggering adaptive response strategies that dynamically alter request sequencing, proxy selection strategies, and TLS handshake fingerprints. The system will integrate long short-term memory (LSTM)-based time series anomaly detection models, allowing the scraping infrastructure to detect and react to adversarial anti-bot defenses before they cause large-scale request errors.
Ultimately, this role requires an unparalleled mastery of asynchronous Python programming, Playwright and Puppeteer-based browser automation, AI-augmented anti-detection engineering, and large-scale adversarial distributed scraping strategies. The Senior Full-Stack Data Engineer Intern will play a pivotal role in engineering Flow’s next-generation AI-powered contact information generation system, ensuring infinite scalability, adaptive stealth capability, and continuous operational resilience against evolving anti-bot countermeasures.
The Senior Full-Stack Data Engineer Intern will be responsible for developing a self-adaptive, continuously evolving, adversarially trained, multi-modal network deception architecture, engineered to execute millisecond-scale request mutations, browser fingerprint randomization, and AI-powered request entropy regulation. The system will leverage multi-layered network obfuscation tactics, cryptographic packet transformations, TLS handshake stealth protocols, and per-request identity perturbation strategies to bypass, evade, defeat, and circumvent all modern anti-bot detection systems, web application firewalls (WAFs), and forensic browser fingerprinting engines. The Senior Full-Stack Data Engineer Intern will implement real-time network deception heuristics, per-session TCP/IP entropy propagation techniques, and AI-driven behavioral mimicry models to ensure that large-scale automated data acquisition operations remain indistinguishable from human traffic across all observable request dimensions.
The Senior Full-Stack Data Engineer Intern will design a dynamic adversarial TLS fingerprint obfuscation framework, leveraging client-side SSL certificate replacement, per-session cryptographic key renegotiation, and ephemeral elliptic curve group selection randomization, ensuring that server-side TLS fingerprinting engines do not establish reliable cryptographic identity linkage across subsequent requests. By dynamically altering cipher suite preferences, JA3 hash signatures, and TLS extension ordering heuristics, the system will artificially inject cryptographic entropy into the TLS handshake sequence, breaking statistical correlation models used for automated bot detection. This will be reinforced with session-aware TLS fingerprint mutation layers, wherein each outbound request autonomously adjusts its cryptographic handshake profile based on prior-response entropy patterns, preventing long-term fingerprint persistence across repeated session engagements.
To further enhance network deception, the Senior Full-Stack Data Engineer Intern will develop an AI-powered, multi-tiered, probabilistic proxy distribution engine, designed to autonomously rotate, classify, and reassign SOCKS5, HTTP/S, and encrypted relay proxies across dynamically shifting network conditions. The system will maintain a high-dimensional, real-time proxy reputation vector space, allowing it to continuously optimize routing decisions based on success likelihood, site-specific anomaly detection trends, and prior engagement history analytics. The proxy selection model will operate within a recursive, deep-learning-driven error prediction pipeline, ensuring that high-risk, low-trust proxies are automatically rotated out of high-value extraction workflows before triggering request bans or detection events.
In conjunction with network deception techniques, the Senior Full-Stack Data Engineer Intern will architect an AI-powered, heuristic-based DOM mutation analysis engine, designed to autonomously parse, classify, and adapt to structurally dynamic HTML, CSS, and JavaScript-based anti-scraping countermeasures. This system will incorporate vision-based DOM clustering, OpenCV-driven visual similarity indexing, and transformer-powered document embedding models, allowing the extraction engine to infer page structure variability patterns and adjust parsing strategies in real-time. The Senior Full-Stack Data Engineer Intern will develop a multi-stage XPath optimization pipeline, capable of automatically reducing selector redundancy, adjusting for dynamically injected class attributes, and inferring element locality relationships based on deep-learned DOM sequencing heuristics.
To counteract client-side JavaScript-based anti-scraping countermeasures, the Senior Full-Stack Data Engineer Intern will implement an event-driven JavaScript execution sandbox, allowing for fully controlled, browser-native code execution interception, dynamic JavaScript payload rewriting, and fine-grained runtime behavior modification. By integrating AI-enhanced JavaScript obfuscation analysis models, per-function AST (Abstract Syntax Tree) transformation engines, and WebSocket-based execution context injection techniques, the system will dynamically neutralize fingerprinting scripts, telemetry beacon injections, and in-browser bot detection triggers before they execute.
At the concurrency level, the Senior Full-Stack Data Engineer Intern will develop a multi-threaded, non-blocking, distributed execution engine, leveraging Python’s asyncio, multiprocessing shared-memory task orchestration, and low-latency, event-driven coroutine switching mechanisms to achieve near-infinite scalability of parallelized browser automation instances. The system will incorporate adaptive request partitioning strategies, hierarchical task allocation, and server-side load-aware resource distribution models, allowing it to scale fluidly across geographically distributed, Kubernetes-managed containerized browser clusters.
Furthermore, the Senior Full-Stack Data Engineer Intern will develop a predictive, AI-driven anomaly detection and recovery framework, leveraging long short-term memory (LSTM) sequence models, time-series autoencoders, and multi-factor anomaly detection pipelines to preemptively identify and neutralize request error patterns before they propagate into large-scale disruption events. The system will continuously monitor scraping session health, analyze rate-limiting trigger points, and dynamically reconfigure proxy routing, request timing jitter, and fingerprint mutation strategies in response to observed adversarial engagement behaviors.
Ultimately, the Senior Full-Stack Data Engineer Intern will be responsible for developing a completely self-sustaining, adversarially hardened, infinitely scalable, AI-driven network deception and automated contact information generation system, ensuring the infrastructure operates at maximum stealth, resilience, and extraction efficiency under the most highly defended adversarial web conditions.
The Senior Full-Stack Data Engineer Intern will be responsible for architecting a massively parallelized, AI-augmented high-frequency request infrastructure, optimized for zero-detection, ultra-high concurrency, and adversarially trained self-learning bot evasion. This system must sustain millisecond-scale automated data extraction operations, continuously bypassing server-side bot mitigation frameworks, JavaScript-based telemetry analysis engines, and WAF-driven rate-limiting countermeasures. The Senior Full-Stack Data Engineer Intern will integrate real-time, AI-powered risk classification models, ensuring that every outbound request is dynamically mutated across multiple obfuscation layers, including TLS entropy propagation, JA3 fingerprint diversification, and multi-modal network fingerprint shuffling. This system will leverage high-dimensional feature embeddings, deep reinforcement learning (RL)-based policy optimization, and Bayesian probability modeling, allowing it to adaptively reconfigure request attributes based on evolving server-side detection trends.
A critical component of this infrastructure is the full automation of CAPTCHA evasion strategies, leveraging self-learning adversarial OCR techniques, deep-learning-driven CAPTCHA type classification, and AI-powered human-mimetic interaction models. The Senior Full-Stack Data Engineer Intern will design a multi-modal CAPTCHA-solving architecture, incorporating ensemble-based vision transformers (ViTs), CNN-powered feature extraction pipelines, and recurrent attention-based sequence decoders, ensuring that CAPTCHA-solving rates exceed human-level performance benchmarks. The system will include real-time CAPTCHA queue management, wherein solving tasks are dynamically prioritized based on difficulty, success probability, and response latency constraints. The Senior Full-Stack Data Engineer Intern will implement a reinforcement learning (RL)-enhanced CAPTCHA error correction feedback loop, wherein previously misclassified CAPTCHA attempts are used to fine-tune adversarial training models, continuously improving accuracy over time.
For text-based CAPTCHA challenges, the Senior Full-Stack Data Engineer Intern will develop a custom hybrid OCR model, integrating Tesseract with LSTM-based character segmentation heuristics and OpenCV-driven morphological preprocessing layers. The system will employ contrast-limited adaptive histogram equalization (CLAHE), Gaussian noise reduction filters, and probabilistic character reconstruction algorithms, ensuring maximum text segmentation fidelity in distorted CAPTCHA scenarios. For image-based CAPTCHA challenges, the Senior Full-Stack Data Engineer Intern will design an ONNX-accelerated convolutional neural network (CNN) pipeline, optimized for low-latency inference on CPU-based execution environments, leveraging multi-layer spatial attention modules and residual block-based feature encoders to achieve sub-50ms image CAPTCHA recognition speeds. For audio-based CAPTCHA challenges, the Senior Full-Stack Data Engineer Intern will implement an OpenAI Whisper-Tiny-powered speech-to-text transcription model, incorporating frequency-domain spectrogram analysis, phoneme probability scoring, and context-aware word reconstruction heuristics.
Beyond CAPTCHA evasion, the Senior Full-Stack Data Engineer Intern will develop an AI-driven, adversarial reinforcement learning (RL) framework for automated bot detection bypass, leveraging deep Q-learning (DQL), proximal policy optimization (PPO), and adversarial imitation learning (AIL) techniques to train nodes capable of autonomously navigating highly defended web environments. The system will incorporate self-supervised learning objectives, evolutionary adversarial retraining cycles, and multi-agent cooperative training paradigms, allowing it to dynamically optimize request sequencing strategies, fingerprint mutation parameters, and engagement persistence probabilities based on live response feedback data.
To ensure that high-frequency automated scraping sessions remain statistically indistinguishable from organic human browsing activity, the Senior Full-Stack Data Engineer Intern will design a behavioral mimicry engine, leveraging reinforcement learning (RL)-optimized human interaction synthesis models, time-series movement anomaly detection filters, and probabilistic session continuity classifiers. This system will dynamically inject human-like cursor movements, keystroke delays, viewport resizing events, and randomized click trajectories, ensuring that automated browser automation sessions exhibit organic user behavior under forensic scrutiny.
At the infrastructure level, the Senior Full-Stack Data Engineer Intern will engineer a multi-threaded, event-driven request orchestration fabric, leveraging asynchronous coroutine-based execution, zero-copy memory-sharing task scheduling, and CPU-optimized concurrent browser rendering pipelines, enabling ultra-low-latency, high-throughput automated data extraction operations. The system will incorporate Kafka-powered, distributed queue-based load balancing mechanisms, ensuring that scraping workloads are dynamically distributed across geographically optimized execution clusters, maintaining persistent session longevity under adversarial network conditions.
Furthermore, the Senior Full-Stack Data Engineer Intern will develop a real-time anomaly detection and auto-recovery framework, integrating autoencoder-based outlier detection models, LSTM-powered anomaly classification networks, and multi-factor session persistence heuristics, allowing the system to preemptively detect and mitigate request error cascades before they propagate into large-scale operational disruptions. The Senior Full-Stack Data Engineer Intern will implement intelligent error prediction models, leveraging ensemble-based gradient boosting classifiers, probabilistic Bayesian error likelihood scoring, and unsupervised clustering-based anomaly segmentation, ensuring that network errors, rate-limiting events, and proxy bans are mitigated in real-time.
Ultimately, the Senior Full-Stack Data Engineer Intern will be responsible for delivering a self-learning, infinitely scalable, fully autonomous, AI-augmented contact information extraction infrastructure, ensuring zero-detection, continuous operational resilience, and maximum stealth persistence under adversarial anti-bot conditions.
The Senior Full-Stack Data Engineer Intern will be responsible for designing, deploying, and maintaining a self-sustaining, AI-augmented, cryptographically obfuscated, adversarially resilient, large-scale automated contact information extraction system that operates without human oversight, continuously learning from live network responses, adapting to evolving anti-bot frameworks, and autonomously modifying its request signatures, browser fingerprints, and network traffic obfuscation strategies. The Senior Full-Stack Data Engineer Intern will leverage multi-layered adversarial deep learning, cryptographic tunneling protocols, reinforcement learning (RL)-based request mutation strategies, and AI-driven network deception heuristics to ensure undetectable, ultra-high-volume automated data extraction under industrial-scale operational loads.
To support this, the Senior Full-Stack Data Engineer Intern will engineer an AI-powered, multi-modal network obfuscation and deep traffic encryption framework, leveraging dynamic SOCKS5 over TLS tunneling, ephemeral OpenVPN relays, Tor onion routing overlays, WireGuard-based multi-hop encrypted session persistence, and TLS fingerprinting-resistant cryptographic handshake obfuscation layers to evade network-based bot detection mechanisms, deep packet inspection (DPI) algorithms, and cloud-based WAF request entropy scoring models. The system will maintain persistent session longevity across hundreds of thousands of dynamically allocated IP addresses, using per-request identity randomization, per-session fingerprint entropy propagation, and per-domain cryptographic negotiation variance to ensure no traceable request linkage across independent scraping instances.
The Senior Full-Stack Data Engineer Intern will develop a predictive, adversarially trained, real-time TLS handshake reconfiguration engine, wherein each outbound request dynamically adjusts its cipher suite preferences, JA3 fingerprint attributes, and TLS extension ordering heuristics, ensuring that server-side entropy analysis models are unable to establish behavioral consistency across repeated request patterns. This system will integrate adaptive TLS handshake mutation layers, wherein high-trust, low-risk request sequences execute with historically validated cryptographic identity profiles, while low-trust, high-risk scraping attempts rotate through randomized JA3 hash perturbation cycles to disrupt heuristic correlation models used for bot detection.
At Layer 7, the Senior Full-Stack Data Engineer Intern will develop a zero-trace adversarially trained web automation framework, incorporating ultra-low-latency Playwright-based Chromium browser rendering instances, full-stack JavaScript execution obfuscation layers, DOM-based fingerprint perturbation heuristics, and AI-driven human-mimetic session simulation models to ensure that automated scraping workflows remain statistically indistinguishable from real human browsing interactions. This will include per-request viewport mutation models, high-entropy WebRTC signature divergence layers, multi-factor behavioral mimicry injection routines, and AI-enhanced human interaction pattern reinforcement modules, ensuring that browser automation instances exhibit organic user engagement characteristics under forensic scrutiny.
To counteract modern client-side bot detection frameworks, the Senior Full-Stack Data Engineer Intern will engineer an AI-powered JavaScript execution interception and response manipulation engine, allowing for deep-level instrumentation of browser execution environments, inline function hook rewriting, and in-memory modification of site-specific telemetry beacons. This will be used to dynamically rewrite tracking scripts, intercept JavaScript-based fingerprinting payloads before execution, and inject randomized execution timing variance into behavioral telemetry logs, effectively circumventing heuristic anomaly detection models that monitor client-side execution consistency.
To achieve hyper-scale, high-frequency parallelized data extraction, the Senior Full-Stack Data Engineer Intern will architect a fully containerized, Kubernetes-orchestrated, infinitely scalable scraping cluster, wherein thousands of isolated browser automation instances execute within dynamically provisioned, ephemeral microservice environments, adjusting CPU and memory allocation parameters based on site-specific response time fluctuation metrics. The system will include AI-powered workload distribution engines, latency-aware network request load balancers, and LSTM-based request failure prediction models, ensuring that scraping workloads are automatically reallocated in response to real-time network congestion, resource depletion, and site-specific anomaly detection events.
To prevent session persistence leakage and request correlation vulnerabilities, the Senior Full-Stack Data Engineer Intern will implement a self-regenerating, probabilistically randomized session identity framework, wherein browser fingerprint attributes, TLS handshake parameters, and network-layer identity heuristics are continuously shuffled across repeated engagements, ensuring that no two scraping sessions share traceable forensic characteristics. This will involve WebGL shader noise injection, randomized AudioContext signature divergence, dynamically generated canvas fingerprint perturbation layers, and AI-augmented entropy diffusion models, ensuring that browser automation instances evade persistent identity tracking techniques used by modern anti-bot frameworks.
At the infrastructure level, the Senior Full-Stack Data Engineer Intern will develop a hyper-efficient, low-latency, multi-threaded request execution pipeline, optimized for high-frequency, concurrent, large-scale distributed data extraction. This system will incorporate asyncio-based coroutine scheduling, multiprocessing shared-memory parallelization, and Redis-backed request caching layers, allowing for massively parallelized, non-blocking task execution across global cloud infrastructure. The intern will engineer a predictive, AI-driven request error mitigation framework, integrating gradient boosting-based request error probability estimators, Bayesian optimization-driven adaptive retry scheduling, and long short-term memory (LSTM)-based time-series error prediction models, ensuring that site-specific anti-bot mechanisms are neutralized before large-scale request error cascades occur.
Beyond automation, the Senior Full-Stack Data Engineer Intern will integrate real-time adversarial training feedback, wherein deep learning models continuously analyze scraping attempts, identify underlying anti-bot countermeasures, and autonomously retrain fingerprint obfuscation strategies, TLS mutation patterns, and AI-powered session continuity heuristics to ensure continuous adaptation against evolving detection algorithms. The system will leverage reinforcement learning (RL)-enhanced adversarial retraining cycles, evolutionary algorithm-based multi-agent optimization heuristics, and ensemble-based anomaly classification models, ensuring that bot detection bypass techniques continuously evolve in response to emerging security measures.
Ultimately, the Senior Full-Stack Data Engineer Intern will be responsible for delivering a self-sustaining, infinitely scalable, AI-augmented industrial-scale data extraction system, ensuring zero-detection persistence, real-time anti-bot adaptation, and continuous operational stealth under adversarial conditions. This role represents the cutting edge of large-scale, AI-driven automated contact information extraction, pushing the boundaries of modern anti-detection engineering, adversarial deep learning, and cryptographically obfuscated network automation strategies.
This opportunity is ideal for senior-level engineers already possessing a Master's degree in Computer Science, with extensive professional industry experience in Python, headless browsers like Puppeteer, Playwright, Selenium, Scrapy, network engineering, data engineering, data science, deep learning, and artificial intelligence. The internship is remote-only, requires a commitment of at least 40 hours per week, and requires a commitment to staying with the company for at least a very bare minimum of 6 full months.
Roles and Responsibilities:
Develop and deploy a fully autonomous, AI-powered, infinitely scalable distributed web crawling and web scraping system that enables real-time, high-volume data extraction from billions of web pages, corporate directories, social media profiles, publicly available records, and beyond.
Engineer an infinite-scale, bot-resistant data extraction system capable of bypassing sophisticated anti-scraping defenses, including JavaScript-based fingerprinting, web turnstiles, Web Application Firewalls (WAFs), advanced CAPTCHA challenges, and behavioral tracking mechanisms.
Implement advanced network obfuscation techniques, including dynamic IP rotation, SOCKS5 proxy tunneling, TLS fingerprint spoofing, JA3 signature evasion, WebRTC masking, and encrypted traffic routing to ensure seamless data extraction without detection.
Develop AI-powered, heuristic-driven entity resolution algorithms to intelligently associate extracted contact data (phone numbers, full names, emails) across disparate, unstructured data sources, improving lead quality and sales conversion potential.
Design and optimize an ultra-low-latency, event-driven data processing pipeline, leveraging multi-threaded concurrent execution, asynchronous coroutines, and Kubernetes-orchestrated browser automation microservices for Playwright, Puppeteer, Selenium, and Scrapy.
Architect a self-healing, AI-augmented proxy management infrastructure that dynamically rotates and scores proxies in real time, selecting high-reputation IPs for sensitive queries while aggressively burning through expendable proxies in high-risk scraping operations.
Deploy AI-powered CAPTCHA evasion models, integrating convolutional neural networks (CNNs), adversarially trained OCR engines, OpenAI Whisper-Tiny models for audio CAPTCHAs, and behaviorally realistic browser interactions for reCAPTCHA and hCAPTCHA bypassing.
Develop intelligent request entropy mutation strategies, ensuring that automated HTTP requests, browser interactions, and API calls exhibit natural variability in headers, user-agent strings, TLS session identifiers, and JavaScript execution patterns.
Create an AI-driven JavaScript execution sandbox, capable of intercepting, modifying, and neutralizing site-side anti-bot scripts, fingerprinting payloads, and dynamically injected honeypot traps.
Build a fully containerized, high-performance distributed scraping cluster, enabling large-scale, globally distributed web crawling workloads using Kubernetes, Docker, Redis task queues, and cloud-based ephemeral compute instances.
Integrate reinforcement learning (RL)-powered anomaly detection models, allowing the scraping system to continuously adapt to evolving anti-bot defenses by analyzing failed requests, identifying adversarial countermeasures, and automatically adjusting evasion strategies.
Optimize scraping workloads using predictive analytics and deep-learning-based decision models, dynamically prioritizing target domains, refining extraction heuristics, and minimizing system resource expenditure while maximizing lead acquisition efficiency.
Develop an ultra-resilient, AI-enhanced error mitigation and request recovery framework, ensuring uninterrupted scraping operations through intelligent auto-retries, session persistence, and error-predictive request dispatching.
Qualifications:
Education: Completed Master’s Degree in Computer Science mandatory.
5-6+ years of professional industry experience in Python, Javascript, network engineering, data engineering, data science, deep learning, and artificial intelligence.
Mastery-level proficiency in Python with deep expertise in asynchronous programming, multiprocessing, coroutine-based parallelism (asyncio), and non-blocking request handling for large-scale data extraction workflows.
Expert knowledge of distributed web crawling, and distributed web scraping frameworks such as Scrapy, Puppeteer, Playwright, Selenium, and BeautifulSoup, with experience in web automation, headless browsers, network request interception, and JavaScript execution management.
Strong understanding of advanced bot evasion and anti-detection engineering, including JA3 TLS fingerprint randomization, browser fingerprint spoofing, HTTP/2+ request entropy manipulation, and multi-modal session persistence strategies.
Extensive experience in large-scale proxy management and network obfuscation, including dynamic IP rotation, residential and mobile proxy pools, SOCKS5 tunneling, VPN relays, and AI-driven proxy scoring algorithms.
Expertise in AI-based CAPTCHA-solving techniques, including adversarial OCR models, CNN-based object recognition, OpenAI Whisper for speech-to-text CAPTCHAs, and browser-native behavioral mimicry strategies for automated human-like CAPTCHA engagement.
Experience with AI-enhanced entity resolution and probabilistic record linkage techniques, leveraging Named Entity Recognition (NER), FuzzyWuzzy string matching, and deep learning-based graph-based contact association models.
Expertise in cryptographic network security techniques, including TLS handshake manipulation, packet encryption, OpenVPN-based traffic rerouting, DNS over HTTPS (DoH) request obfuscation, and AI-driven traffic pattern deception.
Deep knowledge of adversarial machine learning, reinforcement learning (RL), and real-time AI-driven bot detection bypass techniques, including deep Q-learning (DQL), proximal policy optimization (PPO), and adversarial imitation learning (AIL).
Extensive experience designing scalable, high-concurrency data pipelines using Kubernetes, Docker, Redis task queues, Celery, Google Pub/Sub, Apache Kafka, and horizontally scalable cloud infrastructure.
Strong understanding of JavaScript execution environments and in-browser security mechanisms, including WebGL fingerprinting, AudioContext entropy detection, canvas fingerprint mutation, and JavaScript-based client-side telemetry monitoring.
Familiarity with distributed computing frameworks, including Spark, Dask, and Ray, for processing petabyte-scale web-crawled datasets efficiently.
Ability to work autonomously in a high-pressure, very technically intense, mission-driven environment, continuously adapting, refining, and iterating on AI-augmented anti-detection scraping methodologies.
Benefits:
Remote native; Location freedom
Professional industry experience in the SaaS and AI industry
Creative freedom
Potential to convert into a full-time position
Note:
This position offers an exciting opportunity to gain valueable hands-on experience in advanced data engineering and data science within a high pressure and innovative environment. Candidates must be self-motivated, proactive, and capable of delivering high-quality results independently. The position provides valuable exposure to cutting-edge technologies and real-world software development practices, making it an ideal opportunity for aspiring senior full-stack Python data engineers and data scientists.
***This is an unpaid internship at this time and is suitable for completed Master's graduates that wants to be a Senior Full-Stack Data Engineer.***
***This is an unpaid internship at this time and is suitable for completed Master's graduates that wants to be a Senior Full-Stack Data Engineer.***
***This is an unpaid internship at this time and is suitable for completed Master's graduates that wants to be a Senior Full-Stack Data Engineer.***
Please send resumes to services_admin@flowai.tech