Vetted LangChain Professionals

“Hands-on implementation-focused candidate with experience owning cloud deployments and putting LLM/RAG workflows into production. They stand out for combining customer-facing deployment ownership with practical AI systems work, including retrieval tuning, hallucination mitigation, production incident response, and document-processing pipelines for messy real-world inputs.”

“Built and deployed a production RAG-based internal knowledge assistant that let analysts query company documents in natural language, using LangChain/LangGraph with Pinecone and a FastAPI service for integration. Emphasizes reliability in production through hallucination mitigation (retrieval tuning + prompt guardrails) and measurable evaluation/monitoring (accuracy, latency, task completion, hallucination rate), iterating based on user feedback.”

“Built an enterprise RAG-based document intelligence system at Freddie Mac for regulatory and financial documents, helping analysts cut search time from hours to minutes while improving retrieval accuracy by ~30%. Stands out for combining LLM product delivery with compliance-grade auditability, production monitoring, and scalable Python/FastAPI service design.”

“Built production AI systems in high-stakes domains, including a medical RAG chatbot focused on reducing hallucinations and a document-processing workflow that automated manual PDF extraction. Demonstrates strong end-to-end ownership across backend services, APIs, LLM integration, and iterative reliability improvements based on real usage and failure analysis.”

“Built and deployed an AI-driven expense categorization workflow integrating OpenAI API and PGVector to automate general ledger coding. Stands out for combining LLM/embedding architecture with finance operations context, stakeholder-facing deployment ownership, and measurable impact of roughly 30%+ reduction in manual coding effort.”

“Growth-focused B2B SaaS operator with hands-on experience improving enterprise adoption for a cloud governance and FinOps platform. They combine customer discovery, ROI-driven messaging, automation, and funnel instrumentation to improve conversion and handoffs, citing an 18% lift in enterprise adoption and roughly $200K-$3M in influenced pipeline.”

“AI and platform engineer with 5 years of experience who built a production knowledge assistant for Verizon end-to-end, from architecture through deployment, monitoring, and incident hardening. Stands out for combining modern LLM/RAG systems with enterprise-grade rigor, including validation layers, observability, versioning safeguards, and measurable impact on technician productivity and retrieval quality.”

“Currently building a DBC (Digital Birth Certificate) agentic AI system to speed root cause investigation for quality issues at their company. They bring hands-on experience designing and leading multi-agent workflows, including orchestrator/root-agent patterns, evaluation agents, clarification agents, and practical guardrails for hallucination, bias, and rate-limit management.”

“Developer with a pragmatic, disciplined approach to AI-assisted coding: uses tools like Copilot, ChatGPT, and Gemini to speed up debugging, optimization, unit testing, and documentation while maintaining ownership of design and code quality. Interested in expanding from single-agent workflows into multi-agent setups for larger coding tasks and stays current through hands-on use and AI ecosystem updates.”

Sai Sri Kolanu

Screened

Mid-level AI Engineer specializing in LLMs, RAG, and production ML systems

Dearborn, MI4y exp

“Built and shipped an AI-powered RAG diagnostic assistant at Ford for EV technicians, integrating GPT-based models with LangChain, FAISS, and SageMaker into real technician workflows. Stands out for combining strong production LLM architecture with practical safety guardrails, monitoring, and measurable impact: 45% better diagnostic accuracy and roughly 30 minutes saved per case.”

“AI/full-stack engineer in gaming analytics who joined Omnic.ai at a 2-person stage, helped grow with the company, and built both backend and frontend for real-time gameplay analysis products. He combines computer vision production experience with LLM/RAG systems work, and has already led 4 employees while shipping 12 models in a fast-moving startup environment.”

Namrata Shivshankar patil

Screened

Mid-level Backend/Full-Stack Engineer specializing in cloud, AI, and distributed systems

Arlington, TX6y exp

“Built and shipped internal AI support systems spanning Angular/TypeScript frontends, Java/Spring/AWS backends, and Claude-powered troubleshooting workflows. Stands out for combining full-stack product delivery with practical LLM engineering, including RAG, structured outputs, production evals, and careful human-in-the-loop safety decisions. Has shipped systems serving 150-800 daily sessions at 99.5% availability while reducing repetitive support burden.”

“Backend-leaning full-stack engineer who has shipped real-time, customer-facing dashboards and ticketing/payment features at Freshworks and Global Payments. Strong in Python API design (Django/Flask/FastAPI) and React/TypeScript UIs, with hands-on experience scaling PostgreSQL for high transaction volumes and operating services on AWS, including incident response and HIPAA-aligned security controls.”

Saumay Killa

Screened

Mid-level Full-Stack Engineer specializing in AI SaaS and web applications

New York, NY3y exp

“Built a career platform feature end-to-end that generates tailored resumes and cover letters using a React/TypeScript frontend, Postgres, and AWS Lambda/SQS backend. Strong in event-driven, serverless architecture and pragmatic product iteration, with a quantified 60% improvement in onboarding completion after redesigning the UX with resume parsing and a multi-step flow.”

Ashok Canchi

Screened

Junior Software Engineer specializing in full-stack and AdTech systems

Sunnyvale, CA4y exp

“Front-end engineer with nearly 2 years of React experience who independently owned a revamp of an internal ad tech platform as the sole front-end engineer. Stands out for handling complex conditional UI flows, collaborating across product/design/backend, and proactively improving usability and performance through techniques like debouncing, virtualization, and TypeScript.”

“Java/Spring Boot engineer with startup-style ownership experience across e-commerce, banking, and healthcare analytics. Stands out for driving a monolith-to-microservices migration with Kafka that improved checkout reliability under peak load, while also contributing full-stack with Angular and supporting production operations end to end.”

“Financial-services software engineer with experience building both full-stack advisor tools and production AI features at Charles Schwab. They owned an automated portfolio rebalancing workflow and helped launch an LLM-powered reporting assistant that cut report-writing time by 70%, while maintaining compliance through strict tool use, evals, and mandatory human review.”

“Backend/full-stack engineer at Qualcomm who built and operated a drift monitoring platform for 10k+ edge AI models. Stands out for combining strong TypeScript/React/Node execution with production-grade systems thinking across PostgreSQL tuning, Redis caching, ECS deployments, and Kafka-based architectural improvements that measurably improved reliability and release speed.”

“Open-source JavaScript contributor focused on performance and maintainability in data visualization libraries—refactored legacy ES5 into modular ES6, added tests/docs, and delivered ~30% faster load times with positive community adoption. Also optimized a React dashboard (~40% load-time reduction) and took ownership in an ambiguous AI product initiative by setting milestones, standing up an initial ML pipeline, and shipping a prototype in ~6 weeks that became the basis for production.”

“Small-team builder of Promethium’s “Mantra” next-gen agentic text-to-SQL engine, using vector DB + LangGraph tooling and SQL validation/evaluation to improve query accuracy. Experienced in diagnosing production LLM workflow failures via LangSmith traces and in running hands-on developer workshops and pre-sales POCs with live debugging and real customer data.”

“Built production LLM systems including a real-time customer feedback analysis and workflow automation platform using RAG and multi-agent orchestration with confidence-based human escalation, addressing privacy and legacy integration challenges. Also automated ML operations with Airflow/Kubernetes (e.g., daily churn model retraining) cutting retraining time to under 30 minutes, and demonstrates a rigorous testing/monitoring approach plus strong non-technical stakeholder collaboration.”

“ML/GenAI engineer with recent CVS Health experience building a production RAG system over unstructured financial/research documents using LangChain, FAISS, and Pinecone, plus LoRA/PEFT fine-tuning of GPT/LLaMA for domain-aware summarization. Demonstrates strong applied MLOps and data engineering skills (Airflow/Prefect, Docker/Kubernetes, CI/CD, MLflow) and measurable impact (sub-second retrieval, ~40% better context retrieval, ~25% entity matching improvement).”

“Full-stack engineer who shipped a group scheduling SaaS feature with live availability updates using Next.js App Router + TypeScript, owning production reliability after launch (auth debugging, monitoring, polling/backoff tuning). Has hands-on experience with Postgres schema/index design and query optimization (EXPLAIN ANALYZE) and building durable orchestrated backend workflows with retries and idempotency.”