Vetted AI Agents Professionals

“AI/backend engineer who has owned production applied-ML systems end to end, including a Jitsi meeting intelligence platform with custom RoBERTa boundary detection, LLM summarization, and automated retraining from user feedback. Also has healthcare AI experience building a diabetes medication titration system with strict validation, drift monitoring, and safety guardrails—showing both product speed and high-stakes engineering rigor.”

“AI/LLM engineer with strong healthcare domain depth who has shipped production-grade agents for care coordination and clinical workflow automation. Stands out for combining Knowledge Graph RAG, LangGraph orchestration, and rigorous eval/guardrail systems to improve reliability in high-stakes environments, with measurable gains in review time, hallucination reduction, latency, and clinician adoption.”

“Product-minded full-stack engineer who has built AI-heavy systems spanning Next.js/TypeScript frontends, Python/FastAPI backends, queues, databases, and workflow infrastructure. Stands out for combining strong technical depth with UX instincts—improving trust in AI assistants, shipping ambiguous client features quickly, and creating reusable primitives for AI generation and analysis products.”

“Product-minded full-stack engineer with experience shipping engagement features and core communication systems at DribbleUp and Expys. Stands out for combining rapid MVP execution with rigorous iteration: delivered a leaderboard feature that lifted engagement by 8% initially and 20% overall, built a chat MVP in 3 days, and has hands-on experience deploying LangChain-based concierge agents with evals and human review.”

“Built full-stack features for an AI healthcare copilot at Prompt Opinion, combining React/Next.js, Node/Express, PostgreSQL, and LLM/MCP integrations over FHIR-compliant patient data. Stands out for healthcare data interoperability knowledge, production ownership, and measurable impact, including scaling chatbot throughput and improving model response accuracy by 30% through better MCP tool architecture.”

Ziyu Wang

Screened

Mid-level Full-Stack Engineer specializing in AI platforms and agents

New York, NY4y exp

“Front-end engineer focused on high-performance real-time browser applications, with hands-on ownership of an AI-integrated education platform and interactive admin tooling. Stands out for deep browser fundamentals knowledge and measurable UI performance gains, including a 40% reduction in client-side response latency.”

“Full-stack engineer who described a sophisticated forum platform spanning Next.js, NestJS, MongoDB, MySQL, PostgreSQL, RabbitMQ, and AWS. Stands out for strong systems thinking: they designed for type safety, eventual consistency, idempotent async moderation, analytics scalability, and production reliability, while also driving an architectural refactor that reduced new post-type delivery from multi-day work to about half a day.”

“Founding Principal Engineer/Head of Engineering at Amberflo (Seed $5M Homebrew; Series A Norwest) who built and shipped an AI Gateway + real-time LLM cost metering/pricing MVP end-to-end (control plane/data plane, AWS infra, CI/CD). Known for extremely fast MVP cycles (often 1–2 weeks), scaling teams (50–60 hires), and driving major pivots (usage-based billing to FinOps) by repurposing an existing metering/pricing platform; based in Chicago and has led a Silicon Valley startup remotely with frequent Bay Area travel.”

“LLM/RAG practitioner who productionized an LLM-driven customer communication and transaction understanding system at PayPal, emphasizing privacy/compliance guardrails and large-scale data normalization. Experienced in real-time debugging of hallucinations via retrieval pipeline tuning and in leading hands-on developer workshops and sales-aligned POCs to drive adoption.”

Yasser Ali

Screened

Junior AI & ML Engineer specializing in agentic systems and full-stack AI products

San Francisco, CA2y exp

“Won a machine learning contest and was placed onto a Kaiser data science team, where they built ML models for hospital bottleneck prediction and resource allocation. They later built and deployed a full-stack LLM-based “data analyst agent” (with custom orchestration plus LangChain/OpenAI Agents experience) that generates analysis code, answers questions, and produces dashboards from uploaded datasets, emphasizing rigorous evaluation sets, robustness, and healthcare security/compliance integration.”

Sandeep Athota

Screened

Mid-level AI/ML Engineer specializing in cloud MLOps and production ML systems

Texas, USA4y exp

“AI/ML engineer at J.P. Morgan Chase who deployed a production financial-risk prediction platform combining CNN/LSTM/gradient boosting on AWS SageMaker, with automated drift-triggered retraining and governance-grade fairness testing. Leveraged SageMaker Clarify plus SMOTE and LLM-generated synthetic data to improve minority-group F1 by 0.12, and communicated results to non-technical risk/ops teams via Power BI dashboards.”

Yukti Kamthan

Screened

Senior Software Engineer specializing in AI/ML and data systems

Mumbai, India10y exp

“Built and shipped production LLM/AI agent systems including an NL-to-SQL query agent with semantic search and Redis-based caching, using schema-aware prompting and threshold validation to reduce hallucinations. Has orchestration experience running ML microservices on Kubernetes and automating event-driven insurance (P&C) workflows (claims/policy + fraud checks), reporting ~60% manual overhead reduction and ~99% uptime, with strong monitoring/drift-detection and business-facing Power BI reporting.”

“Built and shipped a production LLM-powered customer support triage/resolution agent that automated ~60% of tickets, cutting response times from hours to seconds and improving first-response resolution by ~40%. Experienced designing multi-tenant, tenant-isolated agent architectures with RAG, schema-based tool calling/strict JSON validation, and strong reliability practices (guardrails, retries, fallbacks, monitoring), including safe integration with messy ERP-like data.”

Qice Sun

Screened

Junior GenAI Software Engineer specializing in multimodal RAG and agentic workflows

Sunnyvale, CA2y exp

“AI/LLM engineer with production experience building a multimodal RAG agent for Walmart driver support, combining hybrid retrieval (dense+BM25) and fine-tuned Llama 3 served via vLLM on Azure AKS to reach sub-second latency. Drove measurable impact (25% fewer escalations, 60% lower token costs, 33% lower storage costs) and also built Kafka-based microservices that cut batch runtime from 2 hours to 15 minutes and reduced DB load by 80%.”

Yash Rangucha

Screened

Mid-level Software Engineer specializing in backend microservices and real-time streaming

Illinois, USA4y exp

“Built and owned an end-to-end LLM-powered enterprise retrieval pipeline at ServiceNow, spanning ingestion of structured/semi-structured sources through vector retrieval and real-time API serving. Focused heavily on reliability and quality (multi-stage validation, monitoring, evaluation pipelines) while also driving performance improvements (~35% faster responses) via caching, async processing, and SQL/query optimization.”

Vamsi Reddy

Screened

Mid-level AI/ML Engineer specializing in healthcare and financial ML systems

Nashville, TN5y exp

“ML/AI engineer with hands-on experience shipping both predictive healthcare models and clinical GenAI assistants into production. They combine strong MLOps depth across Azure and AWS with healthcare-specific safety thinking, including PHI guardrails, retrieval grounding, and production monitoring, and they also built internal Python tooling for fraud ML workflows at Capital One.”

Saumya Pailwan

Screened

Junior Software Engineer specializing in applied AI and audio ML

Evanston, IL2y exp

“Engineer with unusually mature experience leading AI-assisted development, including orchestrating multiple coding agents across a data pipeline feature as if managing a small engineering team. Stands out for balancing aggressive adoption of AI tools with disciplined judgment around architecture, security, and merge quality, and for translating that experience into stronger tech leadership.”

“Product leader working at the intersection of AI, data governance, and industrial/energy operations. They have led a major finance-platform rewire for solar and wind asset forecasting at NextEra and previously built an AI-assisted field service app for fire alarm technicians across four business units, showing unusual depth in both enterprise data architecture and safety-critical mobile workflows.”

“Full-stack product builder with strong GTM and applied AI experience, including end-to-end ownership of a production lead intelligence platform that combined React/TypeScript, Python services, external data enrichment, and LLM orchestration. Notably reduced SDR research time from 15-20 minutes to under 2 minutes per account and also drove an 8% revenue increase at Finding Pi by building a customer segmentation framework from analysis of 45k+ users.”