Vetted Multi-Agent Systems Professionals

“ML/AI engineer with hands-on ownership of production recommendation and GenAI systems, spanning experimentation, deployment, monitoring, and iteration. Stands out for delivering measurable outcomes—22% CTR lift, 15% conversion lift, and a 30% reduction in support tickets—while demonstrating strong judgment on latency, cost, and safety tradeoffs in real-world systems.”

“Built and deployed an AI agent to help patients navigate complex housing information by scraping and normalizing unstructured data across all 50 U.S. states, then layering a LangChain RAG system with MMR re-ranking to reduce hallucinations. Experienced in orchestrating multi-agent workflows (LangGraph/CrewAI) and production reliability practices (Pydantic-validated outputs, LLM-as-judge evals, tracing). Also delivered stakeholder-facing explainability via SHAP dashboards for a loan-approval predictive model at Welspot.”

Uttam Kumar

Screened

Intern AI Engineer specializing in LLM agents, RAG, and scalable cloud deployment

Atlanta, GA2y exp

“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.”

Sakshi Patel

Screened

Mid-level Software Engineer specializing in e-commerce and supply chain platforms

Remote, USA3y exp

“AI-focused developer who has built several practical AI products, including EchoMate, a voice-agent system designed to act as a proxy for doctors and support patients when physicians are unavailable. Also has experience with multi-agent/API-based workflows in a solar suitability project, showing interest in applying AI across both healthcare and climate-related use cases.”

George Platon

Screened

Principal Full-Stack Engineer specializing in AI, DevOps, and cloud platforms

Romania, Romania16y exp

“Built a production end-to-end AI video-to-reels clip extraction system using a multi-agent architecture with transcription, captioning, effects generation, and centralized orchestration. Demonstrates unusually strong systems thinking around reliability, observability, evaluation, and production tradeoffs for LLM-powered workflows, including Kubernetes/Kafka-based deployment and regression-driven prompt governance.”

Kevin Delong

Screened

Senior AI/ML Engineer specializing in Generative AI, LLMs, and RAG systems

Irvine, CA12y exp

“AI/ML engineer with hands-on experience shipping production systems across fintech, travel, and legal use cases. They’ve built end-to-end chatbot, generative content, and RAG solutions on AWS with CI/CD, monitoring, and guardrails, including a loan application platform that generated $3,000 in sales in its first month.”

“Built and shipped “LabMate,” a production AI assistant specialized in laboratory hardware, using a weighted multi-source RAG pipeline with reranking and reasoning-focused query decomposition to handle complex user questions. Deployed on a local GPU cluster with vLLM and NVIDIA MPS (plus OCR/VLM components), and established evaluation using synthetic + public reasoning datasets while collaborating weekly with non-technical admins to align requirements and resource constraints.”

“At onedata, built and deployed an LLM-powered, multi-agent analytics platform on AWS Bedrock that lets users create Amazon QuickSight dashboards through natural-language conversation, cutting dashboard build time from ~30 minutes to ~5 minutes. Strong in production concerns (observability, token/cost tracking, model tradeoffs) and in bridging business + technical work, owning pre-sales pitching through delivery with an engineering management background focused on AI product management.”

Manvi Panjwani

Screened

Mid-level Machine Learning Engineer specializing in cloud, governance automation, and distributed systems

San Francisco, CA4y exp

“Governance engineer intern at GSK who built policy-as-code automation using Open Policy Agent/Rego integrated into GitHub CI/CD and Terraform workflows. Also built and shipped a voice-enabled expense tracking app using speech-to-text + LLM structured extraction with strong validation, retries, and semantic guardrails, and designed the supporting PostgreSQL data model with performance-focused indexing.”

Ranxin Li

Screened

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

San Jose, USA2y exp

“Built and deployed Clyra.AI, an AI-driven daily scheduling product that uses a LangGraph-based multi-agent LLM pipeline (task extraction, verification, reflection) grounded with strict RAG over emails/documents/calendars and real-world signals like health metrics. Designed a custom agent orchestrator with bounded loops/termination conditions and a self-auditing verification/reflection layer to reduce hallucinations while controlling latency and cost via caching and model distillation.”

SREYAS GANGJI

Screened

Mid-level Software Engineer specializing in AI/ML backend systems

Chicago, IL4y exp

“AI/data engineer at ZS Associates focused on production-grade agentic systems, FastAPI microservices, and cloud-native ETL/RAG pipelines at significant scale. They’ve built multi-agent validation and diagnostic workflows inspired by their Copilot/KUBEPILOT AI work, supporting 500K+ records per day while improving ML inference performance by ~30% and cutting manual troubleshooting by 60%.”

“GenAI/LLM engineer who built and deployed a production RAG system for enterprise document search and decision support, cutting manual lookup time by 40%+. Experienced with LangChain/LangGraph agent orchestration plus Airflow/Prefect for ingestion and incremental reindexing, with a strong focus on reliability (testing, observability) and stakeholder-driven metrics.”

“LLM/agentic AI builder who led end-to-end integration of an LLM system into a business intelligence product, creating a scalable, metadata-driven RAG/agent pipeline with an orchestrator that routes queries to specialized agents (including DB-backed quantitative querying). Also built an LLM-to-SQL chatbot and partnered with non-technical stakeholders to capture domain context and improve SQL generation, using automated LLM-based testing to evaluate reliability.”

“AI engineer with production experience building a LangGraph-based, stateful multi-agent system at MetLife to automate complex insurance claims adjudication, integrating document discovery, Azure Document Intelligence OCR/extraction, and health data analysis. Strong in agent orchestration and production deployment (Docker + FastAPI REST APIs), with a structured approach to reliability, evaluation, and stakeholder-driven requirements.”

Ramya Sree Kanijam

Screened

Mid-level Software Engineer specializing in LLM, RAG, and cloud AI

Corpus Christi, TX3y exp

“Recent master’s graduate who led a team project building an LLM-based chatbot with RBAC-controlled information disclosure and a focus on reducing hallucinations. Also has hands-on embedded robotics experience (Arduino obstacle-avoiding robot using ultrasonic sensors) and practical DevOps/cloud deployment exposure with Docker, Terraform, Jenkins, and AWS (EKS/ECS/CodePipeline).”

Butchi Venkatesh Adari

Screened

Mid-level Machine Learning Engineer specializing in LLM platforms and robotic perception

NewYork, NY4y exp

“Built and shipped a production multi-agent personal financial assistant at AlphevaAI on AWS ECS, combining FastAPI microservices, Redis/SQS orchestration, and Pinecone-based hybrid RAG (semantic + BM25) to ground financial guidance. Improved routing accuracy with an embedding-based SetFit + logistic regression intent classifier feeding an LLM router, and optimized UX with live streaming plus cost controls via model tiering and caching.”

Akshay Katageri

Screened

Mid-level AI Engineer specializing in multi-agent systems and RAG

Jersey City, NJ4y exp

“Built and shipped a production LangGraph-based multi-agent LLM analytics/decision copilot that answers questions across SQL/BI systems and unstructured docs, emphasizing grounded, tool-verified outputs with citations and confidence gating. Deep hands-on experience with orchestration (LangGraph, CrewAI, OpenAI Assistants, MCP) plus real-world latency/cost optimization (vLLM batching/KV caching, speculative decoding, quantization) and rigorous eval/observability. Partnered closely with business/ops stakeholders to deliver explainable reporting automation, cutting manual reporting time by 50%+.”

Farida Poor

Screened

Junior Machine Learning Engineer specializing in NLP and multimodal transformers

Bay Area, CA3y exp

“Built and deployed LLM-powered agentic chatbot and text-to-SQL systems using LangGraph/LangChain (and Bedrock), structuring workflows as DAGs with planning/replanning and validation to improve tool-calling reliability and reduce hallucinations. Operates production feedback loops with online/offline metrics, drift detection, and LangSmith-based evaluation pipelines, and regularly partners with business stakeholders and clinicians using slide decks and visual charts.”

“Founding/early engineer experience across Asante and a Series A startup (Adgency), shifting from data science/ML into owning production full-stack systems end-to-end. Built core product flows (registration, business profiles, map service), AWS-deployed gRPC microservices with CI/CD, and operated low-latency agent/video ad generation workflows with retries/fallbacks and PostHog-based observability.”

“Backend-focused engineer with hands-on experience deploying AI-driven document processing and RAG-based workflows using Python, LangChain, FAISS, and REST APIs. Has owned projects from requirements through post-launch monitoring, including debugging production retrieval issues and building reliable pipelines for messy PDFs/scans and compliance-oriented document analysis.”

“Backend/ML engineer who built a job-search automation SaaS using a modular Selenium ETL pipeline, rigorous testing/observability, and a cost-optimized two-pass LLM ranking approach. Has led high-integrity data extraction from messy multi-city PDF records (95% integrity) and managed modular production rollouts for a 20+ engineer team, with a strong security focus (deny-by-default, row-level access control) in an AI-assisted grading platform.”

“Software/ML engineer who built a production automated job-finding and cold-email personalization system for Fortune 500 outreach, using JobSpy for dynamic scraping, LangChain orchestration, and LLM+vector DB semantic search with grounding/relevance metrics and guardrails. Also delivered a predictive investment analytics platform for financial advisors, communicating results via Tableau dashboards and portfolio KPIs like Sharpe ratio and drawdowns.”