Vetted Web Scraping Professionals

“AI/backend engineer who has built multiple applied AI systems end-to-end, including an underwriting document intelligence copilot, ambient clinical documentation workflows, and a financial analysis agent. Stands out for combining practical LLM architecture choices with reliability mechanisms like human-in-the-loop review, eval frameworks, and grounded retrieval in production settings.”

Nikita Papenkov

Screened

Mid-level Full-Stack Engineer specializing in frontend, Web3, and data visualization

Russia7y exp

“Frontend engineer with hands-on ownership of a complex blockchain launchpad UI, including a solo refactor that unified Solana and Monad flows under one architecture. Their work emphasized feature-flag-driven multi-chain support, real-time data handling, and major bug reduction, with team feedback strong enough that they nearly received a promotion.”

Lenny Lin

Screened

Junior Full-Stack Engineer specializing in AI and distributed systems

Champaign, IL3y exp

“Built and owned a hackathon project (Gritto) with a Python/FastAPI backend that routes user text through a sequence of Gemini agents to produce structured JSON outputs. Has hands-on production deployment experience using Docker/Docker Compose, GitHub Actions CI/CD, AWS App Runner, MongoDB, and secrets management (Doppler + migration to AWS Secrets Manager), plus implemented a chat-like experience via multiple HTTP requests when SSE wasn’t viable.”

“ML/LLM engineer with production experience building medical RAG systems to automate chart review, including retrieval + re-ranking and rigorous evaluation. Notably uncovered errors/bias in physician-curated ground truth by tracing answers back to source note chunks and presented evidence to an academic partner, accelerating deployment. Also built a RAG-based FAQ chatbot for a health insurance company and delivered it to non-technical stakeholders via demos.”

“Built and shipped “LabMate,” a production AI assistant specialized in laboratory hardware, using a weighted multi-source RAG pipeline with reranking and reasoning-focused query decomposition to handle complex user questions. Deployed on a local GPU cluster with vLLM and NVIDIA MPS (plus OCR/VLM components), and established evaluation using synthetic + public reasoning datasets while collaborating weekly with non-technical admins to align requirements and resource constraints.”

prashansa chaudhary

Screened

Mid-Level Full-Stack Software Engineer specializing in cloud-native data platforms and AI apps

Redwood City, CA4y exp

“Software engineer who has owned customer-facing/internal platforms end-to-end, emphasizing fast iteration through small releases backed by monitoring and rollback safety. Built SurveyAI with reusable React/TypeScript components and a stateless Node.js REST backend with clear API contracts/validation, and created an internal Airflow + AWS Lambda automation tool integrated with Slack alerts to reduce manual work and improve response time.”

George Stathopoulos

Screened

Entry-level Software Engineer specializing in full-stack web development and applied systems work

New York City, NY0y exp

“Full-stack developer with hands-on experience building an end-to-end automated trading platform that combines web scraping, relational data storage, Flask/React architecture, and LLM-based decisioning via Google Vertex AI. Also brings production experience at CALEC, where they contributed frontend improvements including welcome-page redesigns, multilingual support, and accessibility-related fixes.”

“Lead full-stack engineer with strong Python backend depth and hands-on React/TypeScript experience, working in startup-like B2B SaaS environments serving enterprise software customers such as CrowdStrike, HashiCorp, and VMware. Stands out for redesigning tightly coupled systems into modular AWS-based microservices and building configurable integration platforms that improved enterprise customer onboarding and marketplace workflow scalability.”

“Hands-on AI/LLM practitioner who built a RAG-based customer support chatbot and tackled production issues like data chunking complexity and response-time lag. Uses techniques such as overlapping chunks, semantic search, context engineering, and query routing, and has experience presenting technical demos/workshops to developer audiences.”

“Robotics software architect who built an automated pick-and-place palletizing prototype at BLACK-I-ROBOTICS, spanning perception (multi-RealSense fusion, segmentation, 6D pose, ICP), GPU-accelerated motion planning (MoveIt 2 + NVIDIA CuRobo), grasp generation, and safety (human detection + safe mode). Also brings cloud/CI/CD depth from VERIDIX AI (AWS Cognito/Lambda/ECS and CodePipeline stack) and demonstrated strong debugging chops by reducing outdoor rover EKF drift to ~5 cm via Allan variance-based IMU tuning.”

“Built and launched “Language AI,” a Next.js/TypeScript app that clones a user’s voice (via ElevenLabs) to deliver language lessons in their own voice, using Supabase for auth/Postgres/storage and hosting backend on Render. Post-launch, identified ElevenLabs voice-clone limits after initial users and reworked the pipeline to store audio assets and delete clones to support more concurrent users; also added Google auth to improve adoption. Previously worked in a high-growth startup environment (Study Park) taking concepts from ideation to production.”

Surya Danturty

Screened

Intern AI/ML Engineer specializing in computer vision and time-series forecasting

Riverside, CA0y exp

“Undergrad who built a production RAG chatbot for a messy college website using OpenAI embeddings + FAISS, overcoming hard-to-crawl/non-selectable site content and strict API budget limits. Applies information-retrieval best practices (section-based chunking with overlap, precision/recall evaluation) and reliability techniques (edge-case testing, similarity thresholds, fallback responses), and has experience scaling similar indexing work to ~300,000 Wikipedia pages.”

“Founding/early engineer experience across Asante and a Series A startup (Adgency), shifting from data science/ML into owning production full-stack systems end-to-end. Built core product flows (registration, business profiles, map service), AWS-deployed gRPC microservices with CI/CD, and operated low-latency agent/video ad generation workflows with retries/fallbacks and PostHog-based observability.”

“Software engineer who built JobIntel, an end-to-end Python ETL pipeline integrating ATS data from platforms like Greenhouse using Scrapy and FastAPI. Stands out for production reliability work: designing async fault-tolerant architecture, optimizing PostgreSQL write-heavy upserts, and building a Prometheus/Splunk observability stack that cut debugging from hours to minutes.”

“Built both a fantasy sports analytics product and a privacy-sensitive AI assistant for therapists, showing range across consumer and healthcare use cases. Particularly notable for designing self-hosted, HIPAA-conscious LLM systems with RAG, structured outputs, observability, and human-in-the-loop guardrails for clinical workflows.”

“Backend/ML engineer who built a job-search automation SaaS using a modular Selenium ETL pipeline, rigorous testing/observability, and a cost-optimized two-pass LLM ranking approach. Has led high-integrity data extraction from messy multi-city PDF records (95% integrity) and managed modular production rollouts for a 20+ engineer team, with a strong security focus (deny-by-default, row-level access control) in an AI-assisted grading platform.”

“Frontend/product engineer who has led end-to-end builds across automotive and healthcare: created a multi-tenant, high-performance Next.js luxury inventory platform and a secure, Stripe-powered sick-note workflow integrated with an EMR. Known for data-driven UX decisions (A/B testing) and pragmatic modernization of critical systems (3DS2 upgrade) with measurable conversion and risk improvements.”

“Built and shipped a production agentic LLM analytics platform that lets non-SQL business users query relational databases in plain English via a RAG + LangChain/LangGraph workflow and FastAPI service. Emphasizes safety and reliability with guardrails (validation/access control), testing/evaluation frameworks, and performance optimization (caching, monitoring, Dockerized scalable deployment), reducing dependency on data teams and speeding analytics turnaround.”

“Software/ML engineer who built a production automated job-finding and cold-email personalization system for Fortune 500 outreach, using JobSpy for dynamic scraping, LangChain orchestration, and LLM+vector DB semantic search with grounding/relevance metrics and guardrails. Also delivered a predictive investment analytics platform for financial advisors, communicating results via Tableau dashboards and portfolio KPIs like Sharpe ratio and drawdowns.”

Akhil Bharadwaj Mateti

Screened

Mid-level Software Engineer specializing in Data Science and Machine Learning

Arlington, Virginia4y exp

“Robotics/AV perception engineer who built a semantic-segmentation road detection system and integrated it into a ROS-based real-time pipeline (ROS bag camera feed to live monitor) achieving ~12 FPS. Strong in practical deployment work: solved multi-library versioning issues (ROS/OpenCV/TensorFlow), containerized the stack with Docker, and optimized inference by shifting runtime to C++ for large latency gains on NVIDIA hardware.”

Andrew Clayman

Screened

Senior Data Scientist specializing in ML, NLP, and production AI systems

Remote8y exp

“Machine learning/NLP engineer with deep Azure stack experience (Data Factory, Databricks/Spark, Delta Lake, Azure OpenAI, Azure AI Search) who built end-to-end production systems for semantic clustering, entity resolution, and hybrid search. Demonstrated measurable gains from embedding fine-tuning (~15% retrieval precision, ~10–12% nDCG@10) and designed scalable, quality-checked pipelines with MLOps best practices.”

Lakshmi Priya Ramisetty

Screened

Mid-level ML & Data Engineer specializing in GenAI, graph modeling, and fraud/risk analytics

Redwood City, CA5y exp

“Built a production AI fraud/risk scoring platform at BlueArc that ingests web business/product/site data, generates text+image embeddings, and connects entities in a graph to detect reuse patterns and links to known bad actors. Optimized for scale with incremental graph re-scoring and delivered investigator-friendly explainability by surfacing the exact signals/relationships behind each score; orchestrated workflows with Airflow and GCP event-driven components (Pub/Sub, Dataflow, Cloud Run) and has recent LLM workflow orchestration experience (retrieval, prompting, scoring).”

Adrian Lawrence

Screened

Executive Product & Technology Leader specializing in AI, analytics, and regulated industries

Atlanta, GA14y exp

“Serial startup product/technology leader who previously exited a company to Green Street and has accelerator experience via Notre Dame’s IDEA Center. Now pursuing a commercial real estate analytics concept focused on deep demand analysis for better capital allocation, with a provisional patent filed and experience supporting VC funds as an operating partner on product vision and strategy.”

“Data engineer focused on large-scale financial data platforms, with hands-on ownership of an AWS + Databricks + Snowflake pipeline processing ~2TB/day. Strong in data quality (Great Expectations), schema drift automation, and production reliability (99.9%), plus measurable performance/cost wins (4h→1.2h, ~25% cost reduction). Also built an async Python crawling/ingestion framework with anti-bot mitigation, retries, and Airflow-driven backfills.”