Vetted LangChain Professionals

Drashti Magia

Screened

Junior AI Engineer specializing in LLMs, multimodal ML, and applied machine learning

San Jose, CA3y exp

“Software engineer with a disciplined, production-minded approach to AI-driven development: uses ChatGPT, Claude, GitHub Copilot, and scoped coding agents to accelerate delivery without giving up architectural judgment. Notably applied a multi-agent workflow on ClinicOps Copilot, using agents for planning, Bedrock/RAG scaffolding, and failure testing while personally owning architecture, grounding quality, and end-to-end review.”

“AI-focused engineer with Infosys experience building Azure/.NET chatbot applications and recent hands-on work with FastAPI/LangChain. Built a hackathon multi-agent legal counsel system showcasing agent orchestration, and emphasizes production readiness via Docker, GitHub Actions CI/CD, pytest automation, and adversarial simulations for auditable AI behavior. No direct robotics/ROS experience to date.”

“LLM/agent engineer who has built production multi-agent systems (LangChain/LangGraph) for enterprise workflows like email and calendar automation, with a strong focus on latency, tool-calling accuracy, and evaluation via LangSmith. Also worked on AI long-term memory using knowledge graphs at VEAI and communicated the approach and tradeoffs to CEO/CTO stakeholders.”

“Built a production RAG chatbot for Worcester Polytechnic Institute that indexes 500+ webpages using FAISS + Llama 3, with strong grounding/hallucination controls (confidence thresholds and citations). Also has internship experience orchestrating multi-step ETL pipelines with AWS Step Functions and delivered a 30x faster fraud/claims triage workflow at Munich Re using association rules and stakeholder-friendly dashboards.”

“Built and deployed a production LLM-powered RAG knowledge system to unify operational/policy information across PDFs, wikis, and databases, emphasizing auditability and low-latency/cost performance. Improved answer relevance at scale by moving from pure vector search to hybrid retrieval with metadata filtering and reranking, and partnered closely with healthcare operations/compliance to define acceptance criteria and human-in-the-loop guardrails.”

“New grad focused on AI systems and agent-based development, with hands-on experience using LLMs as a coding partner and building RAG-based document processing workflows. Stands out for practical experimentation with semantic chunking, retrieval optimization, and multi-agent architectures, including redesigning a RAG workflow by adding a reasoning agent to improve response accuracy and reliability.”

“Candidate has hands-on experience at Cognizant building production-grade automation and integration solutions across Python ML services, Java microservices, Kafka, and Selenium-based UI testing. They stand out for a strong reliability mindset—covering failure modes, observability, flaky test hardening, and translating ambiguous payment-system business processes into resilient end-to-end automated workflows.”

“Healthcare-focused ML/AI engineer who has built production voice agents and clinical question-answering systems end-to-end, from experimentation through deployment, observability, and iteration. Particularly strong in making LLM systems reliable in real workflows via RAG, fine-tuning, guardrails, evaluation pipelines, and shared Python tooling; cites ~20% clinical QA accuracy gains and ~40% faster physician decision turnaround.”

“Full-stack engineer with payments/settlement domain experience who modernized a payment tracking workflow from REST to GraphQL and delivered a production payment status dashboard using Next.js App Router + TypeScript. Strong in performance and reliability work (Postgres indexing/Explain Analyze, Redis caching, Datadog observability) and in durable event-driven processing with Kafka (DLQs, idempotency, reconciliation, event replay).”

“Backend/data engineer with hands-on production experience across Python/Flask microservices and AWS serverless/data platforms (Lambda, DynamoDB, S3, Glue/PySpark). Demonstrated strong reliability and operations mindset (JWT/RBAC, retries/timeouts/circuit breakers, CloudWatch/SNS alerting) and measurable performance wins (SQL report runtime cut from 10 minutes to 30 seconds). Seeking ~$150k base and cannot travel for onsite meetings for the next 5–6 months due to family medical constraints.”

“AI/ML engineer who led Impacter AI’s production deployment of a specialized outreach LLM (CharmedLLM) fine-tuned on GPT-4.1, cutting API costs ~40% while boosting outreach effectiveness ~60%. Built the supporting MLOps and data infrastructure (MLflow, Kubernetes, PySpark, Kafka) and has agentic AI experience from University of Dayton, using LangChain + RAG and vector search (Pinecone) to improve reliability and reduce hallucinations.”

“GenAI/LLM engineer with production experience at Allstate building an end-to-end document intelligence workflow for insurance operations—automating document intake, classification, and risk signal extraction. Emphasizes high-reliability design for regulated/high-stakes outputs using schema enforcement, confidence thresholds, validation rules, and human-in-the-loop routing, with metric-driven offline evaluation and production monitoring.”

Mohammed Saalim Kartapillai

Screened

Senior Backend/Full-Stack Engineer specializing in data platforms and cloud microservices

Chicago, Illinois7y exp

“Backend engineer who built and shipped an end-to-end AI outreach product (LazyMails) combining a LinkedIn-scraping Chrome extension with a FastAPI/Postgres backend and Gemini-powered email generation, achieving major personal productivity gains. Also has enterprise experience at TCS on Humana’s 500k+ user wellness platform running Kubernetes microservices with Azure DevOps CI/CD, plus Kafka-based real-time eligibility event streaming and GitOps-driven operations.”