Vetted LangChain Professionals

Shuchi Shah

Screened

Senior Software Engineer specializing in Applied AI and FinTech

San Jose, CA12y exp

“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.”

“Startup-focused full-stack engineer who has worked across fintech and digital health, including Pivotxy and Cybele Health. They combine backend/API development with AI integration, including GPT-powered financial reporting and a finance agent benchmark, and have helped turn manual report workflows that took weeks into outputs generated in minutes.”

“Senior backend/data engineer who has built and maintained HIPAA-compliant, real-time clinical FastAPI services on AWS, orchestrating ML/LLM and vector DB calls with strong reliability patterns (auth, timeouts/retries, graceful degradation, idempotency). Also delivered AWS IaC/CI-CD (Terraform/Helm/GitHub Actions) across EKS/Lambda/SageMaker and built Glue/Spark ETL with schema evolution and data quality controls, plus demonstrated large SQL performance wins (15 min to <9 sec) and hands-on incident ownership.”

“Built and deployed a "personal second brain" product (CloneMind) with an end-to-end RAG pipeline for retrieving information across PDFs, URLs, images, and audio using Next.js/Node.js/Postgres/Supabase/Redis. Demonstrates strong practical depth in retrieval quality tuning, latency reduction via caching, and stateful orchestration with LangChain/LangGraph, plus experience persuading a non-technical professor stakeholder by shipping a working prototype.”

“Graduate-school lab engineer who built and owned the final architecture of a Microservices Hub that integrates REST APIs, issues API keys, monitors 10+ Linux servers, and visualizes service dependencies via a topology graph. Strong in bridging legacy and modern stacks (Dockerized and non-Dockerized services like Apache/screen) using deep Linux/networking knowledge, plus practical real-time audio streaming for STT/TTS and experience mentoring others.”

“Backend Python engineer who owned the full backend build of an AI-driven platform for UK golf clubs, including FastAPI microservices, vector search, and a tuned LangChain+Pinecone RAG pipeline focused on cost and hallucination reduction. Experienced deploying Django/FastAPI/Flask stacks on AWS-backed Kubernetes with GitOps/ArgoCD-style delivery, plus executing legacy-to-AWS migrations and building Kafka-based real-time analytics pipelines.”

CharanKumar Pathakamuri

Screened

Entry-Level GenAI/LLM Engineer specializing in agentic systems and RAG

Baltimore, MD1y exp

“LLM/AI agent engineer with consulting/contract experience (Kanhaiya Consulting LLC) who deployed a production AI agent to automate BIM list workflows end-to-end—from database understanding and data cleaning to automated visualizations/dashboards. Worked around restricted real-time data access by generating synthetic data and improving outputs via supervised fine-tuning, and uses AWS-based LLMOps observability (Opic/OPEC) plus hybrid retrieval (vector+BM25 with reranking) to optimize relevance, latency, and cost.”

Dhruv Bansal

Screened

Intern Software Engineer specializing in backend systems and distributed data pipelines

San Francisco, CA1y exp

“LLM engineer with production experience building end-to-end document processing workflows that unify layout analysis, OCR, and downstream LLM reasoning. Has implemented reliability features (retries, robust error handling, OpenTelemetry logging) and built agentic systems using LangChain/CrewAI, including a student research-paper assistant, while collaborating closely with PMs and non-technical end users to reduce technical debt and simplify architectures.”

Vishnu Priyan Sellam Shanmugavel

Screened

Mid-Level Applied AI Engineer specializing in LLM services, RAG, and OCR/NLP extraction

Arlington, VA4y exp

“Backend/platform engineer who built and evolved a large-scale healthcare document processing system (OCR + LLM orchestration) in Python/FastAPI on Google Cloud (Cloud Run, GCS, Firestore), processing ~1.5M files per batch and tens of millions overall. Emphasizes reliability and operational safety via deterministic IDs, idempotent state machines, strong observability, and self-healing reconciliation, plus disciplined migrations using dual-run validation and incremental rollouts.”

“Unity mixed-reality developer who shipped ZenPlay, a multiplayer Go app on Meta Quest, integrating a C# rules engine with XR input, Meta avatars, Hathora-hosted matches, and Vivox voice chat (reported ~700 MAU). Also built a production LLM agents backend (LangChain + RAG with Pinecone/ChromaDB + ChatGPT) powering embodied conversational avatars, with a strong focus on streaming voice latency optimization (ElevenLabs TTS) and cross-platform WebXR delivery (Quest/iOS/Android).”

Pathapati aditya

Screened

Mid-level Design Engineer specializing in product design and front-end implementation

New York City, NY4y exp

“Product designer-builder who codes, with a rare end-to-end profile spanning product strategy, UX, design systems, frontend engineering, and AI integration. Built PetCloud/PetClub as a fully solo product in Next.js/TypeScript/Supabase and has also driven measurable UX gains in company settings, including 25% higher engagement, faster design-to-dev handoff, and fewer UI bugs.”

“Built and owned a production RAG-based internal knowledge assistant end-to-end, from experimentation through cloud deployment and monitoring. Demonstrated strong practical GenAI judgment by choosing prompt optimization and retrieval tuning over fine-tuning for dynamic data, driving a 40% to 50% reduction in time to answer while improving relevance, lowering hallucinations, and increasing productivity.”

“Built an end-to-end agentic population health strategy copilot for healthcare leadership, turning broad chronic disease questions into structured, evidence-backed strategy briefs. Stands out for combining healthcare domain knowledge with production-grade GenAI implementation, including LangGraph orchestration, Databricks/MLflow deployment, human review, and quality gates focused on citations, metrics, risks, and safety.”

“Backend/AI engineer who built an enterprise RAG chatbot over 40,000+ technical documents, owning the system from ingestion and retrieval design through launch, optimization, and incident prevention. Stands out for treating LLM reliability as a data, retrieval, and observability problem—delivering 90%+ benchmark accuracy, ~50% fewer hallucinations, and major gains in lookup speed and latency.”

Narayan Anantha Krishnan

Screened

Junior AI/ML Engineer specializing in LLMs, RAG, and cybersecurity

Syracuse, NY2y exp

“AI/full-stack builder with hands-on experience shipping conversational and agentic products, including a travel itinerary assistant, a multi-agent data analysis platform, and a self-correcting RAG system. Also brings academic research depth from Syracuse University, where they helped develop tiny-LLM-based IoT threat mitigation and presented an accepted paper at FLAIRS 39.”

SIVA RAMAKRISHNA PALADI

Screened

Mid-level AI/Full-Stack Engineer specializing in agentic AI and RAG systems

Santa Clara, CA5y exp

“Solo builder who shipped two ambitious AI products from scratch: Zoly, a healthcare/pharmacy automation platform with voice agents, RAG, clinician dashboard, and patient app live in 4 months, and Breeth, a contextual memory system for AI agents deployed on AWS. Particularly compelling for teams needing a hands-on full-stack/AI engineer who can operate in ambiguity, design for safety and compliance, and turn complex agent workflows into production products.”

“New grad software engineer who has already built both a full-stack location-based social app and an internal AI on-call copilot using OpenAI and LangChain. Stands out for combining end-to-end product execution with practical LLM engineering, including RAG, fallback design, citations, and production evals, plus shipping a hackathon-winning MVP in 24 hours.”

“Software engineering intern who built and deployed a full-stack telemedicine platform (React/Node/MongoDB) used daily in a pediatric clinic, incorporating PyTorch-based predictive features. Demonstrated strong customer-facing iteration and production performance debugging—resolved a live slowdown by indexing/optimizing MongoDB queries and adding caching, improving response times by ~50%.”

“Student technologist building production-oriented AI products, including a college guidance chatbot for Track2College and a voice-based travel assistant. Strong hands-on experience with RAG systems, FastAPI backends, TypeScript frontends, retrieval evaluation, and tool-using LLM workflows, with a clear focus on grounded, reliable user experiences.”

“Built AI-powered products across both healthcare and financial services, including a privacy-conscious assistant for elderly health check-ins and a production RAG system for high-stakes financial document analysis. Stands out for combining full-stack engineering with strong LLM reliability practices—grounding, structured outputs, fallback handling, monitoring, and human-in-the-loop controls—while delivering measurable impact on accuracy, speed, and system performance.”

“Backend engineer (Encore) who led the evaluation and redesign of a high-volume, low-latency real-time retrieval/ranking and inference platform on AWS, shifting from tightly coupled services to a modular architecture for better fault isolation and independent scaling. Strong focus on production reliability, observability, and security (JWT/RBAC, multi-tenant scoping, Postgres/Supabase RLS), with disciplined migration playbooks (feature flags, shadow traffic, dual writes/reconciliation).”

“Built a production multi-agent RAG assistant using LangChain/LangGraph with OpenAI embeddings and FAISS, focusing on retrieval quality and latency (Redis caching, parallel retrieval, precomputed embeddings). Experienced orchestrating ETL/ML pipelines with Airflow and Databricks Workflows, and has delivered an AI assistant for business ops to extract insights from policy/compliance documents through close non-technical stakeholder collaboration.”

“Built production LLM/agent systems for procurement and contract spend controls, including a proactive contract value leakage detection platform that moved an organization from reactive audits to pre-payment rejection. Combines multi-agent orchestration (Semantic Kernel/LangChain/AutoGen), document AI benchmarking (Textract vs Azure DI), and MLOps/testing (MLflow, QTest/Pytest) with strong security practices (RAG-grounded responses to prevent prompt injection). Integrated anomaly alerts directly into SAP SES workflows and Power BI dashboards, citing ~$38M leakage addressed across large spend environments.”