.png){kind=logo}
Vetted Data Scientists in New York
Pre-screened and vetted in New York.
Mid-level AI/ML & Generative AI Engineer specializing in LLMs and MLOps
Mid-level Applied AI Scientist specializing in multimodal LLM and document intelligence systems
Mid-level Applied AI/ML Engineer specializing in scalable generative model infrastructure
Harsh Patel
Senior Data Scientist specializing in LLM applications, RAG systems, and production ML
“Senior Data Scientist in consulting who has built production RAG systems for insurance/annuity document search at large scale (100K+ PDF pages), emphasizing grounded answers, guardrails, and low-latency retrieval. Experienced in end-to-end MLOps for LLM apps—monitoring, evaluation sets, drift handling, and safe rollouts—and in orchestrating complex pipelines with Prefect/Airflow and deploying services on Kubernetes.”
Senior Data Scientist specializing in machine learning and battery R&D analytics
Mid-level AI/ML Engineer specializing in LLMs, RAG, and agentic AI systems
Mid-level AI Engineer specializing in LLM automation and RAG systems
Venkatalakshmi Kottapalli
Mid-level AI/ML Engineer specializing in LLMs, RAG pipelines, and MLOps
“LLM engineer/data analyst who built a production RAG QA assistant over the Jurafsky & Martin NLP textbook to reduce hallucinations and provide explainable, source-grounded answers. Experienced with LangChain/LangGraph orchestration, retrieval optimization (embeddings, vector DBs, caching), and rigorous evaluation/monitoring (Retrieval@K, A/B tests, telemetry/drift). Previously communicated analytics insights to non-technical stakeholders at GS Analytics using Power BI and simplified reporting.”
Mid-level Generative AI Engineer specializing in LLMs and RAG for enterprise and FinTech
Junior Backend Engineer specializing in Python, Kubernetes, and Kafka streaming
Jeet Choksi
Mid-level Machine Learning Engineer specializing in real-time AI and data platforms
“ML/NLP engineer who has built production systems end-to-end: a real-time recommendation platform (100k+ profiles) using BERTopic-style clustering and a RAG-based news summarization/recommendation stack with ChromaDB. Strong focus on scaling and reliability (GPU batching, Redis caching, Kafka ingestion, Docker/Kubernetes, Prometheus/Grafana) and on maintaining model quality over time via drift monitoring and retraining triggers.”
Mid-level AI Engineer specializing in LLM agents, RAG, and evaluation