Vetted LangChain Professionals

“Backend/applied AI engineer from Cloud Rack Systems who built production GenAI/RAG and data platforms on Azure/Databricks at enterprise scale (2.5M records/day). Known for making LLM systems behave like deterministic services via strict retrieval contracts, citation-based validation, and strong observability—shipping a knowledge assistant used daily by 50+ users while driving hallucinations near zero and materially improving latency and cost.”

“Software engineer with enterprise CPQ/CRM/ERP integration experience (Argano) who owned an end-to-end pricing preview capability deployed on AWS Kubernetes with Jenkins CI/CD and full observability (Prometheus/Grafana). Also built an AI-native research agent using LangChain + Chroma to filter academic papers, reporting ~15 hours/week saved for a professor.”

“Full-stack engineer who has delivered production customer analytics/dashboard features using Next.js App Router + TypeScript on the frontend and Java Spring Boot microservices on the backend. Demonstrates strong production ownership (monitoring latency/error rates/adoption) plus hands-on performance work across React rendering and Postgres query/index optimization, and has implemented Temporal-like durable workflows with retries and idempotency.”

“Built and deployed a production RAG-based clinical decision support assistant at MedLib, focused on fast, trustworthy answers from large medical documents. Demonstrates deep practical experience improving retrieval accuracy (semantic chunking + metadata-aware search), controlling hallucinations with grounded generation and thresholds, and adding clinician-requested citations using chunk metadata, with evaluation driven by healthcare professional review.”

“Data/ML platform engineer with ~6 years in financial services and enterprise data platforms, building regulated fraud/credit-risk pipelines on AWS (Airflow, EMR/Spark, MLflow) and an Azure lakehouse ingesting 50+ sources and serving ~100M records/day. Also led an early-stage deployment of a RAG-based internal AI search tool using AWS Bedrock and LangChain with automated evaluation to validate LLM accuracy.”

“Backend engineer specializing in event-driven/serverless systems and Python/FastAPI APIs. Built a scalable PDF-to-structured-data pipeline on AWS (S3, Lambda, Step Functions, Textract, DynamoDB, SNS) with strong observability (p50/p90/p99) and reliability patterns (idempotency, retries/DLQs), and has led zero-downtime migrations using feature flags, dual writes, and incremental rollouts.”

“Full-stack engineer with deep ad-tech/marketing automation experience, building production tools that reduce programmatic ad waste and improve search ads performance. Shipped and operated AWS-deployed, Dockerized systems with Postgres/Redis and strong observability (Datadog/OpenTelemetry), and delivered measurable impact (25k campaigns processed, 50k sites negated, 3–4 hours/week saved). Built scalable abstractions for multi-platform ad integrations, enabling rapid onboarding of additional clients.”

“Built a real-time AI meeting assistant using a Chrome extension that streams audio to a backend LLM workflow with transcription and RAG, then hardened it for production with queue-based streaming, async pipelines, security controls, and full observability. Also has hands-on startup sales experience, partnering with customers to define measurable technical win conditions (latency/accuracy) to close deals and drive adoption.”

“Customer-facing software engineer who builds and deploys practical AI/RAG solutions (e.g., an AI assistant for searching billing PDFs) by deeply understanding support workflows and iterating with users. Demonstrates strong production instincts—quickly stabilizing peak-traffic API timeouts with caching/background jobs, then implementing durable fixes with proper monitoring and maintainable code practices.”

“LLM engineer/data analyst who built a production RAG QA assistant over the Jurafsky & Martin NLP textbook to reduce hallucinations and provide explainable, source-grounded answers. Experienced with LangChain/LangGraph orchestration, retrieval optimization (embeddings, vector DBs, caching), and rigorous evaluation/monitoring (Retrieval@K, A/B tests, telemetry/drift). Previously communicated analytics insights to non-technical stakeholders at GS Analytics using Power BI and simplified reporting.”

“Forward Deployed Engineer at EasyBee AI who productionized a self-storage customer’s multi-agent LLM system end-to-end—rebuilding it with LangGraph/CrewAI, integrating with real property management + CRM systems via an MCP server, and adding observability/guardrails for reliable daily use. Experienced in live troubleshooting of agentic workflows, developer demos/workshops (including an open-source project, MerryQuery), and partnering with sales to close deals through customer-specific technical demos and fast integration feedback loops.”

“Internship at Discovery Education building a production LLM/RAG chatbot that let marketing and sales teams query and interpret Looker/BI dashboards in natural language, with responses grounded in compliance and state education standards. Emphasizes rigorous evaluation (faithfulness/precision/recall/latency) plus user-feedback analytics, and used LangChain for orchestration, chunking/context-window control, and integration with enterprise sources like SharePoint.”

“Data scientist who has built production LLM agents (GPT-4o + LangChain + RAG) to automate analyst-style ad hoc CSV analysis with guardrails and GPT-as-a-judge evaluation. Also delivered an explainable healthcare NLP system for ICD code classification by collaborating closely with clinicians, using a hybrid rule-based decision tree + BERT model to reach 97% accuracy and cut manual review time.”

“Full-stack engineer with fintech payments experience who shipped an end-to-end guest invoice payment flow emphasizing reliability under retries/failures (idempotency via DynamoDB, async processing with Lambda/EventBridge/SQS + DLQ). Also built a FastAPI backend with Cognito/JWT + scoped guest tokens and a polished React/TypeScript checkout UX, and has performance-focused Postgres/Redis design experience for flash-sale e-commerce workloads.”

“Built and shipped production AI search and RAG features for a university portal, including an embeddings-based semantic search layer and a documentation-grounded assistant with citations and anti-hallucination prompting. Also developed scalable, reliable data pipelines integrating Google Ads/GA4/Meta APIs for automated reporting, with strong focus on evaluation loops and retrieval quality improvements (hybrid search, chunking, query-log driven iteration).”

“Built and shipped a production multi-agent LLM system at Solena that automated internal project intake, validation, reporting, and stakeholder communications using Python, SQL, and LangChain, with strong emphasis on reliability (structured validation, safe defaults, logging, and state tracking). Also used LangGraph to orchestrate a multi-step video summarization pipeline, and has experience partnering with non-technical stakeholders to define “completion” criteria and reporting needs.”

“Data science professional who has built a production RAG-based LLM question-answering system ("Flash Query") to deliver fast, accurate answers over large document collections, focusing on retrieval quality and grounded responses. Also collaborates with non-technical retail/jewelry stakeholders to turn business questions into predictive models and dashboards for decision-making.”

“Full-stack engineer who has shipped and operated generative-AI chat/QA features end-to-end, including a RAG-based pipeline with guardrails and cost/latency monitoring in production. Experienced with React/TypeScript + Node/Postgres architectures, Dockerized deployments to AWS (EC2) via GitHub Actions CI/CD, and building reliable ingestion/ETL systems with idempotency, backfills, and reconciliation.”

“Backend engineer with experience building an end-to-end civic tech AI platform that ingests city council meeting videos, transcribes them with Whisper, and enables natural-language Q&A via a LangChain/FAISS RAG pipeline. Demonstrated strong systems thinking by tuning retrieval for accuracy/latency/memory (cutting response time ~3s→1s and memory ~500MB→25MB) and by safely migrating an ERP from monolith toward services using dual writes, reconciliation, and idempotency to protect financial workflows.”

“Backend engineer from FoodSupply.ai who built and evolved a scalable restaurant/supplier product and order management platform using Node.js and REST APIs. Implemented a hybrid MySQL+MongoDB data architecture, optimized performance with Redis/Prisma, and led a phased migration with feature flags and a temporary sync layer to maintain data consistency. Strong focus on production security (OAuth2, RBAC, row-level security, AWS IAM) and reliability practices (testing with Pytest, Docker/AWS pipelines).”

“Built and deployed a production RAG-based multilingual analytics assistant for healthcare operations, enabling non-technical teams to query claims/EHR and risk metrics with grounded explanations. Demonstrates strong end-to-end LLM system engineering (retrieval tuning, re-ranking, hallucination controls, verification layers) plus workflow orchestration (Airflow/Composer/Step Functions) and stakeholder-driven iteration via prototypes and dashboards.”

“Graduate Research Assistant in Robotics at Arizona State University who built an end-to-end LLM-driven task execution framework enabling collaborative robots to convert high-level natural language instructions into safe, executable ROS actions. Implemented robust monitoring, failure detection, and automatic replanning, and addressed real-world issues like timestamp/frame-transform mismatches and heterogeneous robot interoperability using adapter nodes.”

“Backend engineer focused on scalable, secure, observable systems—built an async workflow backend with REST APIs and state persistence that improved reliability under concurrent load and cut end-to-end processing time ~40%. Strong in production security for multi-tenant systems (OAuth2/JWT, RBAC, DB row-level security) and in low-risk migrations using feature flags and canary releases, including catching and preventing cross-tenant data access issues with CI-based RLS tests.”