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Vetted AI & Machine Learning Professionals in the NYC Metro
Pre-screened and vetted in the NYC Metro.
Intern AI/ML Engineer specializing in LLM agents, RAG, and applied ML
Mid-level Machine Learning Engineer specializing in MLOps, NLP, and quantitative trading
Mid-level AI/ML Engineer specializing in financial services and generative AI
Senior Full-Stack AI/ML Engineer specializing in LLM agents and RAG systems
Mid-level AI/ML Engineer specializing in financial risk, fraud detection, and NLP
Mid-level AI/ML Engineer specializing in forecasting, anomaly detection, and enterprise ML pipelines
Mid-level Machine Learning & Biostatistics professional specializing in healthcare AI
Senior AI/ML Engineer specializing in LLM, NLP, and conversational AI
Principal AI Engineer specializing in enterprise AWS and GenAI architecture
Executive Growth & Product Leader specializing in marketplaces, FinTech, and PLG
Mid-Level Software Engineer specializing in AI platforms and backend systems
Senior AI/ML Engineer specializing in GenAI, LLMs, NLP, and MLOps
Mid-level Machine Learning Engineer specializing in NLP, recommender systems, and MLOps
Senior Machine Learning Engineer specializing in Generative AI and NLP
Senior Data Engineer specializing in Cloud Data Platforms and Generative AI
silin liu
Mid-level AI/ML Engineer specializing in LLM agents, RAG, and enterprise ML systems
“Built a production multi-agent recommendation/RAG system for internal data analysts to speed up weekly report creation by improving document discovery and automating report/SQL generation. Implemented LangGraph-based orchestration with deterministic agent routing, robust error handling (interrupt/resume), and metadata-driven semantic chunking for diverse PDF/document formats, plus monitoring for latency, throughput, and token/cost efficiency.”
Fatim Majumder
Junior ML research engineer specializing in evaluation platforms and applied machine learning
“ML/LLM infrastructure engineer who built and shipped a production internal evaluation + failure-analysis agent (Arthur AI / R3AI context) that orchestrated end-to-end benchmarks with deterministic lineage, regression detection, and root-cause reporting at 5,000+ benchmarks/week. Also built backend observability and data validation systems for analytics pipelines at FullStory processing ~3.4B weekly events, emphasizing schema validation, quarantine fallbacks, and idempotent operations.”
Aarushi Mahajan
Mid-level AI/ML Engineer specializing in NLP, Generative AI, and MLOps
“Internship experience shipping production AI systems: built an end-to-end RAG platform (Python/FastAPI + LangChain/LangGraph + vector search) to answer support questions from unstructured internal docs, with a strong focus on hallucination prevention through confidence gating and rigorous offline/online evaluation. Also delivered an AI-driven personalization/analytics feature using an unsupervised clustering pipeline, iterating with PMs to align statistically strong clusters with actionable business segmentation.”
Utkarsh Srivastava
Junior Machine Learning Engineer specializing in LLMs, RAG, and medical imaging
“At Fileread, the candidate built and deployed an LLM-powered legal document classification and retrieval layer for an agentic extraction system that turns unstructured legal PDFs into structured tables with line-level citations. They productionized a RAG-style pipeline (ingestion, embeddings, retrieval, reranking, generation) and report 95%+ F1 across 70+ legal categories, emphasizing rigorous evaluation and close collaboration with legal domain experts for high-stakes precision.”
Amaan Elahi
Mid-level Software Engineer specializing in backend, AI, and full-stack systems
“Built and shipped production LLM agents including an internal RAG-based compliance classification system at SAIL (FastAPI/Redis/Docker) designed to handle real failure modes and scale to ~10k LLM calls/hour, achieving ~93% pipeline accuracy with reduced hallucination risk via multi-model orchestration and strict grounding. Also architected “Elara,” a state-machine-driven conversational appointment booking agent using structured JSON outputs and backend function execution for reliability, and has experience normalizing messy OTA/PMS data at RateGain.”