Vetted LangChain Professionals

“Built and scaled an AI-powered voice/chat patient engagement platform at Penn Medicine from early prototype into production clinical workflows, focusing on latency, edge cases, and user trust. Strong in LLM reliability engineering (structured prompts, validation/fallbacks), real-time troubleshooting with observability, and cross-functional enablement through pilots, demos, and sales/customer partnership.”

“Founding member of Tausight, building AI systems to detect and protect PHI for healthcare organizations; helped take the company through post–Series A funding and exited after ~6 years. Drove a strategic collaboration with Intel’s OpenVINO team—becoming the first to deploy it in a real production system and improving model performance by ~30% on customer Intel-CPU machines.”

“Backend-focused intern who built and refactored the backend for an LLM-driven gifting mobile app using FastAPI, tackling high-latency LLM + product-API workflows. Implemented async worker-pool/queue processing with Redis caching plus retries/fallbacks, cutting end-to-end suggestion latency from ~4–5 seconds to ~1 second while improving reliability and rollout safety via staged migrations and testing.”

“AI/LLM engineer with published work who built FinVet, a production financial misinformation detection system using multi-pipeline RAG, confidence-based voting, and evidence-backed outputs (F1 0.85, +37% vs baseline). Also built NexusForest-MCP, a Dockerized Model Context Protocol server exposing structured global deforestation/carbon data via SQL tools for reliable LLM tool use. Previously delivered borrower risk-rating (PD) models at BMO Financial Group that were validated and integrated into an enterprise credit system through close collaboration with credit officers and portfolio managers.”

“Software engineer/product owner who has led end-to-end delivery of AI and content-management platforms, including building RAG-based reliability improvements and migrating fragile systems to containerized AWS ECS/Kubernetes with Terraform-managed CI/CD. Experienced designing event-driven microservices (SQS/SNS/RabbitMQ), scaling queue consumers with autoscaling, and creating internal Python tooling to standardize data connectors (e.g., BigQuery/Airtable/internal APIs) to speed iteration.”

“Built and shipped a production RAG-based enterprise knowledge assistant to replace slow/inaccurate search across millions of documents, using LangChain orchestration with GPT-4/LLaMA and vector databases. Strong focus on production constraints—latency, hallucination control, and cost—using hybrid retrieval, guardrails, LLM-as-judge validation, and model routing, and has experience translating non-technical stakeholder pain points into measurable outcomes.”

“AI engineer with production experience building multilingual speech-to-speech translation pipelines (ASR + LLM) for enterprise/media, focused on reliability at scale. Has hands-on orchestration experience (including IBM Watson contexts) and emphasizes production evaluation/monitoring using a mix of traditional metrics and LLM-based evaluators to catch quality regressions while balancing latency and cost.”

“Built and productionized an enterprise retrieval-augmented generation platform for internal knowledge over large unstructured corpora, emphasizing trust via strict citation/grounding and hybrid retrieval (BM25 + FAISS + cross-encoder re-ranking). Demonstrates strong scaling and cost/latency optimization through incremental indexing/embedding and index partitioning, plus disciplined evaluation/observability practices. Has experience operationalizing pipelines with Airflow/Databricks/GitHub Actions and partnering closely with risk & compliance stakeholders on auditability requirements.”

“Built and deployed a production SecureAIChatBot (RAG-based) for secure internal information retrieval, using embeddings/vector search, GPT models, monitoring, and safety filters. Focused on real-world production challenges like latency and output consistency, applying caching, retrieval scoping, smaller models, and controlled prompting, and used LangChain to orchestrate the end-to-end workflow.”

“Software engineer who owned an Order Management System end-to-end at Reliance Jio, improving large-table performance via UI virtualization shipped behind feature flags and refined through direct ops-user observation. Also built an OCR automation tool at Piramal Realty using Python/Tesseract with validation and manual correction fallbacks, driving adoption by operations teams. Experienced integrating with Kafka-based microservices and improving observability using structured logging and correlation IDs.”

“AI/LLM engineer who has shipped 10+ production applications, including InvestIQ on GCP—a production-grade RAG due-diligence engine that ethically scrapes web/PDF sources, builds a ChromaDB knowledge base, and delivers analyst-style dashboards plus a citation-backed chat copilot. Deep focus on reliability (evidence-only answers, hard citations, refusal gating), retrieval tuning, and orchestration (Airflow/Cloud Composer), plus multi-agent systems (CrewAI with 7 specialized finance agents).”

“Built and productionized an internal LLM-powered search tool that helps engineers find the right SolidWorks macros using plain-English queries, using OpenAI embeddings and ChromaDB with strong logging/fallback safeguards. Experienced in diagnosing RAG/agentic workflow issues in real time and in hands-on API support, including fixing customer macros after SolidWorks version updates and driving adoption through reusable solutions and best practices.”

“BNY Mellon engineer who has built and operated production AI systems end-to-end: a LangChain/Pinecone RAG platform scaled via FastAPI + Kubernetes to 1000 RPM with 99.9% uptime, supported by monitoring and data-drift detection. Also deep in data/infra orchestration (Airflow, Dagster, Terraform on AWS/EMR/EC2), processing 500GB+ daily and delivering measurable reliability and performance gains, plus strong compliance-facing model explainability using SHAP and Tableau.”

“Backend engineer currently building an AWS Lambda/FastAPI inventory recommendation system using a LangChain + GPT-4 RAG pipeline and MongoDB vector search; drove major cost optimization via Redis caching (60% reduction) while sustaining 10k+ daily requests under 2s latency. Previously deployed Node.js microservices on AWS OpenShift with Jenkins/Helm at UnitedHealth Group and led a zero-downtime monolith-to-microservices migration at Verizon, including RabbitMQ-based real-time messaging with DLQs and idempotency.”

“JavaScript/Node.js engineer who contributes to open-source utilities focused on API integrations and JSON validation, including a 30–35% throughput improvement by profiling and optimizing deep-clone-heavy code paths. Strong in performance tooling (Node performance hooks, Chrome DevTools flame graphs), incremental/test-driven changes, and community-facing issue triage plus developer-friendly documentation.”

“LLM/agentic workflow engineer with healthcare domain experience who built a HIPAA-compliant multi-agent RAG system for clinical review automation at UnitedHealth Group, achieving 92% precision and cutting latency 40% through async orchestration and Redis semantic caching. Also has strong data engineering orchestration background (Airflow on AWS EMR with Great Expectations) and a proven clinician-in-the-loop feedback process that improved model faithfulness by 18%.”

“Built and led deployment of an autonomous, self-correcting multi-agent knowledge retrieval and validation system at HCA Healthcare to reduce heavy manual research/validation in clinical/compliance documentation. Deeply focused on production reliability and cost—used LangGraph StateGraph orchestration plus ONNX/CUDA/quantization to cut GPU costs by 25%, and partnered with the Compliance VP using real-time contradiction-rate dashboards to hit a 40% automation goal without compromising compliance.”

“Full-stack engineer focused on productionizing LLM applications, including an Android privacy-policy risk summarization app (Kotlin/React Native + FastAPI + Ollama) that cut response times from ~10s to ~5–6s via batching, caching, async, and event-driven architecture. Currently at PRGX building an LLM-based legal contract clause extraction system, partnering closely with legal/procurement SMEs to create schemas, labeled datasets, and evaluation pipelines that improved accuracy from 70% to 85%. Also has experience architecting real-time voice/LLM systems with streaming microservices (Kafka, Kubernetes, gRPC/WebSockets) and an avatar chatbot pipeline (TalkingHead, Google TTS, AnythingLLM).”

“Built and deployed an LLM-powered claims-document summarization system (insurance domain) that cut agent review time from 4–5 minutes to under 2 minutes and saved 1,200+ hours per quarter. Hands-on across orchestration and production infrastructure (Airflow retraining DAGs, Kubernetes, SageMaker endpoints, FastAPI) and recent RAG workflows using n8n + Pinecone, with a strong focus on reliability, cost, and explainability for non-technical stakeholders.”

“AI/LLM engineer with production experience in legal tech: built a GPT-4 + LangChain RAG summarization system at Govpanel that reduced legal case-file review time by 50%+. Previously at LexisNexis, orchestrated end-to-end Airflow data/AI pipelines processing 5M+ legal documents daily, improving ETL runtime by 35% with robust validation, monitoring, and SLAs.”

“Built and shipped production generative AI/RAG assistants in regulated financial contexts (S&P Global), automating compliance-oriented Q&A over earnings reports/filings with grounded answers and citations. Experienced across the full stack—AWS-based ingestion (PySpark/Glue), vector retrieval + LangChain agents, GPT-4/Claude model selection, and production reliability (monitoring, caching, retries) plus rigorous evaluation and regression testing.”

“Built a production, real-time clinical documentation system at HCA that converts doctor–patient conversations into structured clinical summaries using speech-to-text, LLM summarization, and RAG. Demonstrated measurable gains from medical-domain fine-tuning (clinical concept recall +18%, ROUGE-L 0.62 to 0.74) while meeting HIPAA constraints via PHI anonymization and encryption, and deployed via Docker/FastAPI with CI/CD and monitoring.”

“Backend engineer who significantly evolved an internal analytics/reporting platform (Python API + Postgres) powering self-service dashboards for product/business teams, focusing on reliability under heavy concurrent load and fast query performance. Demonstrates strong production engineering practices across API design (FastAPI), observability, incremental rollouts with feature flags, and data security using JWT/RBAC plus Postgres row-level security.”