Vetted LangChain Professionals

“Backend engineer with insurance/claims domain experience who modernized legacy claims processing systems to support AI-assisted claim review. Emphasizes production-ready API design in Python/FastAPI (schemas, async, caching, graceful degradation), strong observability with Prometheus, and layered security including JWT auth plus database row-level security (Supabase/Postgres).”

“Full-stack + ML engineer who built “Finsight,” a real-time financial risk platform (React/FastAPI/MongoDB/AWS Lambda) processing 2M+ records monthly, using sharding and Redis caching (60% DB load reduction) plus async and batch optimizations. Also has healthcare product experience at Apollo Healthcare, partnering directly with clinicians/admins to design and iterate EHR dashboards via Figma prototyping and user testing, and demonstrates clear system design thinking for real-time voice-to-LLM architectures.”

“Backend engineer who built and evolved the real-time networking/messaging backend for a cross-platform professional networking app (Make Connexions), optimizing for low-latency delivery, privacy, and strong consistency. Experienced scaling Python/FastAPI APIs with async + Redis, and leading safe refactors via versioned endpoints, feature flags, and backward-compatible migrations; strong production auth/RLS expertise including refresh-token rotation edge cases.”

“Built a production AI-powered customer support Q&A system using an internal knowledge base to reduce repetitive ticket work and improve customer satisfaction, with an emphasis on source-backed answers and expert oversight. Also has experience defining deployment services in a microservices architecture and integrating large-scale APIs (including work connected to US HHS/COVID-19).”

“Backend-focused Python engineer who has owned production FastAPI services deployed on Kubernetes, including CI/CD (GitLab CI to ECR) and GitOps delivery via ArgoCD/Helm. Has hands-on experience with complex reliability and infrastructure work—solving data inconsistency with validation/partial-data paths, fixing K8s liveness issues via lazy loading, and supporting a phased cloud-to-on-prem migration with dual-writes and monitoring. Also built Kafka-based real-time ingestion consumers handling bursty, high-throughput traffic with async processing and topic/retention tuning.”

“AI/LLM engineer at GPT integrators who built a production multi-agent enterprise workflow integration system, tackling hard problems in agent orchestration, layered memory, and custom RAG over enterprise/user data. Also built an education-focused agent solution integrating with Canvas, Zoom, and email to automate classroom admin tasks, and is currently applying agentic AI to insurance underwriting workflows in collaboration with underwriters.”

“Internship experience shipping production AI systems: built an end-to-end RAG platform (Python/FastAPI + LangChain/LangGraph + vector search) to answer support questions from unstructured internal docs, with a strong focus on hallucination prevention through confidence gating and rigorous offline/online evaluation. Also delivered an AI-driven personalization/analytics feature using an unsupervised clustering pipeline, iterating with PMs to align statistically strong clusters with actionable business segmentation.”

“Software engineer (Yellow.ai) who built and productionized an AI-driven resume tailoring system using embeddings + Chroma RAG + QLoRA fine-tuning, deployed via Docker/Kubernetes with CI/CD on a CPU-only Oracle VM. Demonstrates strong reliability/evaluation rigor (custom hallucination/coverage/relevance metrics) and measurable business impact, including a 60% user satisfaction lift from improving chatbot intent accuracy with product and support teams.”

“Built and deployed a production enterprise LLM-powered RAG assistant for the construction domain, enabling natural-language querying across PDFs/reports and structured sources (SQL/CSV). Implemented an agent-based routing and multi-agent orchestration approach (LangChain/LangGraph) to reduce hallucinations, improve latency, and deliver actionable, structured responses based on stakeholder feedback.”

“Internship project at Builder Market: built an end-to-end production multimodal LLM application that estimates renovation/replacement costs from appliance photos (CLIP embeddings) or text descriptions, combining fine-tuning with agentic RAG. Focused heavily on real-world performance constraints—latency and cost—using parallel agent workflows, model routing to smaller/open-source models, re-ranking, and retrieval chunking, and collaborated closely with CEO/co-founders to deliver the solution.”

“AI/ML Engineer at Fifth Third Bank who has shipped production fraud detection and risk analysis systems combining ML models with LLM-powered insights/explanations, including real-time monitoring, drift detection, and automated retraining under regulatory explainability constraints. Also built a hybrid-retrieval internal knowledge-base QA system (+20% top-5 relevance) and delivered a customer support chatbot that reduced first response time by 30% through strong stakeholder collaboration.”

“Built and productionized an agentic LLM automation system for an insurance client to determine medication eligibility, using prompt-chaining plus a RAG pipeline over policy rules and deploying on AWS (Lambda/Step Functions, Bedrock) with a serverless architecture. Addressed major data/schema mismatch issues via a semantic matching pipeline and validated performance through human agreement scoring, A/B testing, KPI monitoring, and confidence-based human-in-the-loop review.”

“Customer-facing software engineer who rapidly turns business requirements into Figma prototypes and PoC applications, using workflow prioritization and frequent client reviews to stay aligned. Has hands-on experience integrating with existing authentication/user APIs, building MongoDB-backed caching, and implementing robust fallback/retry mechanisms. Comfortable working on-site with customers and resolving production issues in AWS (e.g., DNS/EC2 traffic routing) in collaboration with DevOps.”

“AI/ML engineer who has shipped production LLM and ML systems, including a RAG pipeline that ingested ~500k insurance/client documents to help adjusters answer questions faster and more consistently. Experienced in handling messy real-world document formats, tuning retrieval/chunking, and reducing latency via vector search optimization, precomputed embeddings, and caching. Also built orchestrated fraud-detection deployment workflows using AWS Step Functions and SageMaker, and partners closely with non-technical operations teams on NLP automation.”

“ML engineer/data scientist who built and deployed a real-time fraud detection platform at Citi on AWS SageMaker, processing 3M+ daily transactions and improving fraud response by 28%. Combines unsupervised anomaly detection (autoencoders) with ensemble models (XGBoost/Random Forest) plus Airflow/Step Functions orchestration, drift monitoring, and explainability (SHAP) to keep models reliable and compliant in production.”

“Built and deployed a RAG-based student/faculty support chatbot at a university that answers from official syllabus/policy documents and now supports 4,000+ students while reducing repetitive support requests. Hands-on with LangChain, LangGraph, and CrewAI to orchestrate reliable agentic workflows, with a strong focus on testing/monitoring in production and cross-functional delivery (e.g., marketing analytics automation at Steve Madden).”

“Built and deployed a production LLM-powered phone assistant for a healthcare clinic, combining streaming STT/TTS with RAG over approved clinic documents and strict safety guardrails to prevent unverified medical advice, plus seamless human handoff. Also has hands-on Apache Airflow experience building robust daily ML/data pipelines with data validation, retries/timeouts, monitoring, and metric-gated model deployment, and iterates closely with clinic staff using real call reviews.”