Vetted AI & Machine Learning Professionals in the NYC Metro

“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).”

“Backend engineer with hands-on experience building real-time, low-latency systems: owned the Python backend for a real-time crowd-monitoring product (top 5% at HackHarvard 2025) using OpenCV, GPU YOLO inference (PyTorch), WebRTC, and OAuth. Also has production Kubernetes/GitOps experience (Helm/Kustomize, GitHub Actions, Argo CD), Kafka-based event pipelines, and executed a minimal-downtime on-prem PostgreSQL migration to AWS EC2.”

“Built and deployed a production Llama 3-based RAG document Q&A system using FAISS, addressing context-window limits through chunking and keeping retrieval accurate by regularly refreshing embeddings. Has hands-on orchestration experience with LangChain and LlamaIndex for multi-step LLM workflows (including memory management) and collaborates with non-technical teams (e.g., marketing) to deliver AI solutions like recommendation systems.”

“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.”

“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%+.”

“Built and deployed a production LLM/RAG clinical document understanding and summarization system for healthcare, focused on reducing manual review time while meeting strict accuracy, latency, and compliance needs. Demonstrates strong MLOps/orchestration depth (Airflow, Kubernetes, Azure ML Pipelines) and a rigorous approach to hallucination mitigation through layered, source-grounded safeguards and stakeholder-driven requirements with physicians/compliance teams.”