Vetted LangGraph Professionals

Mayank VYAS

Screened

Mid-level AI/ML Engineer specializing in LLM agents, RAG retrieval, and IoT ML systems

Tempe, AZ4y exp

“Built production LLM-driven products including a job-hunt AI (job ranking + resume optimization) and an InterviewAI agentic pipeline using LangChain. Focused on practical deployment concerns like securing OpenAI usage via rate limiting and tiered quotas, and demonstrates an applied approach to choosing models, retrieval methods (RAG), and prompting strategies.”

Karan Baid

Screened

Intern Machine Learning Engineer specializing in Generative AI and RAG systems

Jaipur, India

“Early-career AI/LLM builder who created and deployed a multi-agent news analysis agent (Patrakarita) using CrewAI, coordinating researcher/analyst roles to turn noisy article URLs into structured, prioritized outputs (claims, tone, verification questions, opposing views). Strong focus on orchestration debugging and reliability evaluation, including measuring hallucination/redundancy and improving reasoning by refactoring pipeline sequencing.”

Sampath Achalla

Screened

Mid-level Python Full-Stack Engineer specializing in AI microservices and cloud data platforms

USA3y exp

“Backend-leaning full-stack engineer in fintech/payments who shipped an end-to-end Stripe payments + webhook system for a financial microservices platform, emphasizing ledger accuracy via idempotency, transactional writes, retries, and DLQs. Also delivered a real-time React/TypeScript payment status dashboard informed by user interviews, and improved production performance by 35% p95 latency through PostgreSQL tuning and Redis caching on AWS.”

Gomathy Selvamuthiah

Screened

Junior Data/AI Engineer specializing in MLOps, real-time pipelines, and LLM applications

Portland, US2y exp

“Built an LLM-driven MLOps agent at SBD Technologies that automated an EV-charging prediction workflow end-to-end, integrating with real-time Kafka/FastAPI systems supporting 120K+ chargers at 99.99% event delivery. Addressed frequent schema drift by implementing SQLAlchemy/Flyway validation (60% reduction in drift issues) and deployed as Kubernetes microservices with GitHub Actions CI/CD; also has Airflow-based ingestion/crawling experience into Snowflake and stakeholder-facing delivery via a Fleetcharge PWA.”

“Product-minded full-stack engineer with experience in fintech identity verification and industrial analytics, focused on turning repeated operational pain points into reusable platforms. Built real-time KYC/KYB dashboards, secure cross-platform web components, and a multi-tenant workflow engine that cut onboarding from 2 weeks to 1 day while materially improving conversion, reliability, and developer speed.”

“AI/backend engineer who independently built and operated an agentic telecom analytics system end-to-end, using LangGraph and Claude to turn natural language into safe SQL in a regulated environment. He combines startup-speed execution with compliance-minded rigor, citing 95%+ NL-to-SQL accuracy, a 30-minute-to-2-minute workflow improvement, and zero-findings support across three regulatory audit cycles.”

Shuchi Shah

Screened

Senior Applied AI Engineer specializing in RAG and full-stack systems

San Jose, CA13y 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.”

“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.”

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.”

“Backend/AI engineer with deep healthcare experience building production Python microservices that turn raw clinical audio into structured notes and insights. They owned systems end-to-end across architecture, launch, monitoring, and incident response, with measurable impact including 40% lower operating costs, 22% better latency, and 99.9% uptime in a regulated environment.”

“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.”

“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 and shipped production LLM agents for real-time, high-concurrency conversational systems, including a RAG-based pipeline with dynamic multi-provider routing and failover that achieved 99.99% reliability and sub-800ms latency. Also architected a UAV telemetry chatbot with tool-calling (anomaly detection/summarization), strict schema validation, and robust eval/monitoring loops, cutting tool-call errors by 30% and reducing operational costs by 90%.”

Ramya Jonnala

Screened

Principal/GenAI Engineer specializing in LLMs, RAG, and MLOps

Plano, TX9y exp

“Built a production AI-powered university marking system that automates question generation and grading from PDF course materials using a RAG pipeline (S3 + Pinecone) orchestrated with LangChain/LangGraph and deployed on AWS ECS via Docker/ECR and GitHub Actions CI/CD. Addressed a key real-world LLM challenge—grading consistency—by implementing rubric-based scoring, retrieval re-ranking, and standardized context summarization, validated against human instructors.”

Riya Stanly

Screened

Intern Data Scientist specializing in machine learning, NLP, and LLM fine-tuning

2y exp

“Built a production-style AI meeting summarization and action-item extraction system (Azure Speech-to-Text + transformer summarization/NER) exposed via a Flask REST API, with explicit guardrails to prevent hallucinated tasks. Strong focus on reliability: modular agent/workflow design, precision-first evaluation with human-validated golden notes, and practical orchestration patterns (tool-augmented agents; ready to scale into Airflow/LangGraph/Prefect).”

HarshaVardhanRao Vemuganti

Screened

Junior AI/ML Engineer specializing in Generative and Agentic AI

Lafayette, Louisiana4y exp

“Built and deployed a production-grade LLM agent for credit management and accounts receivable automation, integrating ERP/MySQL data via a RAG pipeline and exposing services through FastAPI with Pydantic-validated outputs on AWS Bedrock. Emphasizes reliability and compliance for financial operations using schema validation and human-in-the-loop review, reporting ~32% reduction in manual work and ~41% improvement in response time/reliability.”

Tharun Chowdary Malepati

Screened

Mid-level AI/ML Engineer specializing in GenAI, NLP, and production MLOps

5y exp

“AI/LLM engineer who built and deployed a production healthcare RAG chatbot ("DoctorBot") with strict medical safety guardrails, an 85% confidence-gated verification layer, and latency optimizations that cut responses from ~8s to ~2–3s. Also worked on finflow.ai to generate finance/banking test cases from BRDs, collaborating closely with non-technical domain stakeholders, and has hands-on orchestration experience with LangChain/LangGraph and agentic evaluation/monitoring practices.”

“Full-stack engineer who has owned both institutional and personal products end-to-end, including Rutgers' myCommunity platform used by 70,000+ students. Particularly strong in production systems, data synchronization, authentication, and third-party API integrations, with a pragmatic approach to shipping, observability, and UI modernization.”

“AI/ML engineer with 9+ years of experience building production recommendation and LLM systems end-to-end, from experimentation through deployment, monitoring, and retraining. Stands out for combining strong MLOps discipline with practical GenAI/RAG implementation, including measurable impact such as ~25% higher engagement on an e-commerce recommender and nearly 30% faster knowledge retrieval from internal documents.”

“ML/AI engineer with hands-on experience building healthcare and fraud-detection systems from experimentation through deployment, monitoring, and retraining. Stands out for combining real-time IoT pipelines, cloud-native MLOps, and GenAI/RAG in regulated healthcare settings, with reported impact including reduced emergency response times and a 25% reduction in manual diagnosis time.”

Kathan Patel

Screened

Mid-level Generative AI Developer specializing in Python and LLM applications

5y exp

“Currently working on Kavia AI, an end-to-end AI coding platform that lets users generate enterprise applications from prompts and existing codebases via SCM integrations. The candidate has hands-on experience across the GenAI stack—prompt engineering, LangGraph-based multi-agent orchestration, RAG, knowledge graphs, FastAPI, and AWS monitoring—with a focus on making software creation accessible to non-technical users.”