Vetted Retrieval-Augmented Generation (RAG) Professionals

“Data-engineering-oriented candidate with hands-on experience building an agentic AI product and operational automation workflows. They described automating inventory-to-ERP discrepancy reconciliation with anomaly detection and daily reporting, and also have practical scraping/automation experience dealing with Cloudflare-protected sites using Selenium and Puppeteer.”

“ML/AI practitioner with hands-on experience shipping production time-series forecasting and RAG-based customer insights platforms in an enterprise setting. At BASF, he improved seed sales forecasting beyond naive baselines using model selection tailored by brand size, and he also led a RAG solution over Salesforce reports, complaints, and surveys that reached 2,000+ users with strong daily engagement.”

Francois Odienne

Screened

Director of Engineering specializing in SaaS platforms, AI-first product development, and telecom

Moorpark, CA28y exp

“Technology leader with venture-backed company experience at LogicMonitor, where they aligned engineering strategy with business growth and long-term platform scalability. Demonstrates strong familiarity with the VC/studio/accelerator ecosystem and a thoughtful founder mindset centered on validating real problems, innovative solutions, and execution capacity.”

Amit Dharam

Screened

Junior AI/ML Software Engineer specializing in backend systems and cloud deployment

Tempe, AZ3y exp

“Built multiple end-to-end automation and data systems, including an Accio RAG pipeline combining PDF parsing, FastAPI, Neo4j, and vector search, plus Selenium-based scraping for a virtual try-on product. Stands out for reliability-minded engineering: automated testing, structured logging, validation layers, and a data-driven approach to debugging flaky automation that improved CI pass rates to over 98%.”

“AI/backend engineer who has owned production applied-ML systems end to end, including a Jitsi meeting intelligence platform with custom RoBERTa boundary detection, LLM summarization, and automated retraining from user feedback. Also has healthcare AI experience building a diabetes medication titration system with strict validation, drift monitoring, and safety guardrails—showing both product speed and high-stakes engineering rigor.”

“Product-minded full-stack engineer who has built AI-heavy systems spanning Next.js/TypeScript frontends, Python/FastAPI backends, queues, databases, and workflow infrastructure. Stands out for combining strong technical depth with UX instincts—improving trust in AI assistants, shipping ambiguous client features quickly, and creating reusable primitives for AI generation and analysis products.”

“ML/AI engineer with hands-on ownership of a production LLM/RAG system at Goldman Sachs, focused on workflow automation and large-scale document search for operational teams. They combine strong MLOps and backend engineering skills with practical GenAI evaluation and safety practices, and cite measurable impact including 22% better task guidance accuracy and sub-second search across millions of records.”

“Data engineer with a Master's in Data Science who has owned 30+ customer-facing K-12 SIS migrations end-to-end, building ETL, validation, and SOP-driven deployment processes in a PII-sensitive environment. Also brings recent hands-on agentic AI experience from a biotech capstone, where they led a production-oriented NLP-to-SQL + RAG support system that handled about 30% of support queries in testing.”

“AI/ML engineer who has built a production text-to-image generation system in PyTorch with an AWS-backed inference setup, focusing on GPU-efficient training and embedding-space architectural choices inspired by recent research (e.g., Meta VL-JEPA). Uses both metric-based evaluation (FID) and human testing to validate real-world visual quality, and can translate technical concepts for non-technical stakeholders.”

“Product-minded full-stack engineer with experience shipping engagement features and core communication systems at DribbleUp and Expys. Stands out for combining rapid MVP execution with rigorous iteration: delivered a leaderboard feature that lifted engagement by 8% initially and 20% overall, built a chat MVP in 3 days, and has hands-on experience deploying LangChain-based concierge agents with evals and human review.”

“Full-stack engineer with enterprise banking experience at Citi and hands-on production AI agent work, including a multi-agent incident analysis pipeline using LangGraph, RAG, and LangSmith. Also built a zero-to-one healthcare operations dashboard spanning hospital workflows and AI-assisted clinical features, suggesting a blend of strong systems engineering and product-minded execution.”

PavanKumar Varkala

Screened

Mid-level Full-Stack Engineer specializing in AI-driven web applications

Jersey City, NJ4y exp

“Built and shipped an AI-driven operational workflow platform at Adobe that handled 12k+ monthly requests using React, Node.js, TypeScript, OpenAI APIs, PostgreSQL, Redis, and RAG. Stands out for combining full-stack product ownership with production-grade LLM architecture, evals, and human-in-the-loop controls, delivering measurable gains including 38% higher accuracy and 40% less manual triage.”

Uma mahesh

Screened

Mid-level AI Software Engineer specializing in Generative AI and healthcare automation

NC, USA5y exp

“Backend/AI engineer with 5+ years spanning ServiceNow, Cognizant, and GE Healthcare, where they built agentic AI and RAG systems for safety-critical medical device workflows. Particularly compelling for teams needing production LLM systems in regulated environments: they combine cloud deployment and evaluation rigor with HIPAA-aware privacy controls, explainability, and measurable business impact.”

Ziyu Wang

Screened

Mid-level Full-Stack Engineer specializing in AI platforms and agents

New York, NY4y exp

“Front-end engineer focused on high-performance real-time browser applications, with hands-on ownership of an AI-integrated education platform and interactive admin tooling. Stands out for deep browser fundamentals knowledge and measurable UI performance gains, including a 40% reduction in client-side response latency.”

“Product leader with 10 years in regulated life sciences who has moved from building compliant manufacturing and validation capabilities at Illumina to shipping live AI products. Unusually combines deep FDA/EU MDR/ISO 13485 domain knowledge, hands-on scientific research experience, and practical RAG-based product development for regulatory and manufacturing workflows.”

“Full-stack engineer who described a sophisticated forum platform spanning Next.js, NestJS, MongoDB, MySQL, PostgreSQL, RabbitMQ, and AWS. Stands out for strong systems thinking: they designed for type safety, eventual consistency, idempotent async moderation, analytics scalability, and production reliability, while also driving an architectural refactor that reduced new post-type delivery from multi-day work to about half a day.”

“Platform-focused engineer/technical program leader working in silicon/wafer validation environments, with hands-on experience securing access to sensitive test results and engineering tooling. Has implemented RBAC/least-privilege controls with Azure Entra ID, Key Vault, PAM and integrated Checkmarx into dev workflows, while also deploying ML services on AKS using Bicep/Helm/Docker and Azure DevOps CI/CD with strong monitoring and incident response practices.”

“LLM/RAG practitioner who productionized an LLM-driven customer communication and transaction understanding system at PayPal, emphasizing privacy/compliance guardrails and large-scale data normalization. Experienced in real-time debugging of hallucinations via retrieval pipeline tuning and in leading hands-on developer workshops and sales-aligned POCs to drive adoption.”

“Development manager focused on developer productivity and platform enablement in a polyglot microservices environment. Drove ~50% productivity gains by evaluating and rolling out AI coding copilots with team training and cross-team demos, and designed a Disaster Recovery framework adopted by 50+ microservice teams. Also led edge-focused Python runtime optimization and relies on heavy test automation to safely execute large refactors during major platform upgrades.”

“Built a production AI-driven QA automation platform using a multi-agent architecture (MCPs + LangGraph) to run parallel website tests across multiple device environments via automated image building and containerization. Currently collaborating with restaurant operators and managers to deliver an agentic restaurant analytics system, emphasizing deep domain discovery with non-technical stakeholders.”

“Completed an Amazon externship building a GPT-4 + RAG pipeline to summarize themes from hundreds of employee reviews for workforce analytics aimed at improving warehouse retention. Emphasizes production-readiness through labeled-data evaluation, source attribution for explainability, human-in-the-loop review, and rigorous data cleaning/observability to debug real-world LLM workflow issues.”

“Engineering manager leading a 15-person team at FBN on the Gridbull platform, shipping a self-serve pricing/quoting tool for structured commodity products using real-time futures market data. Owns architecture and reliability for third-party data integrations (WebSocket + REST fallback), including resolving a day-one production incident caused by undocumented vendor connection resets. Introduced lightweight Technical Implementation Plans to improve cross-functional alignment and delivery speed in a high-growth environment.”

“Full-stack engineer with fintech/trading domain experience (Fidelity) and startup SaaS CRM/billing platform work (Zoho), building real-time portfolio analytics and trade-processing systems. Strong in microservices, event-driven architectures (Kafka/WebSockets), and AWS/Kubernetes operations with measurable performance gains (~34–35% latency reduction) and maintainability improvements (~40% faster deployments). Targeting a founding full-stack engineer role in NYC with meaningful equity.”

“Software engineer with strong end-to-end ownership across frontend, backend, data, and infrastructure, including real-time systems (Kafka/Postgres) and observability (Datadog). Built and productionized an AI-native RAG support assistant (OpenAI embeddings + Pinecone) with prompt/guardrail design, achieving 48% agent adoption and 30% faster responses. Experienced in legacy modernization and reliability work using feature flags, event/transaction replay, and rapid embedded delivery.”