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Vetted AI & Machine Learning Professionals in New York
Pre-screened and vetted in New York.
Senior Software Engineer specializing in Generative AI and distributed systems
Senior Machine Learning Engineer specializing in large-scale AI systems
Senior Machine Learning Engineer specializing in Generative AI and NLP
Christopher Sipola
Senior Machine Learning Engineer specializing in LLMs and recommendation systems
“ML/GenAI engineer who owned major parts of Spotify’s AI DJ from offline experimentation through deployment, monitoring, and iteration. They combine recommender systems, RAG, real-time feedback loops, and LLM safety/orchestration to ship consumer-facing personalization features that drove double-digit engagement and deeper listening sessions.”
Senior AI/ML Engineer specializing in LLMs, RAG, and multimodal systems
Senior Data Scientist specializing in Generative AI and LLM evaluation
Senior Applied Scientist specializing in LLMs, GenAI systems, and AutoML
Mid-level AI/ML Engineer specializing in LLM training, RAG, and low-latency inference
Kenil Tanna
Staff-level Machine Learning Engineer specializing in LLMs and MLOps for Financial Services
“Machine learning/NLP practitioner at J.P. Morgan who led development of a production RAG system and an entity resolution pipeline for complex financial data. Deep hands-on experience with embeddings (Sentence-BERT), vector search (FAISS/pgvector), LLM fine-tuning (LoRA/PEFT), and rigorous evaluation (human-in-the-loop + A/B testing) backed by strong MLOps on AWS (Docker/Kubernetes, MLflow, Prometheus/Datadog).”
Krishna Reddy
Mid-level AI/ML Engineer specializing in fraud detection and clinical LLM assistants
“Built and deployed a production clinical support LLM assistant at Mayo Clinic using a LangChain-orchestrated RAG architecture (Llama 2/PaLM) over de-identified clinical records, integrating BigQuery with Pinecone for semantic retrieval. Focused on healthcare-critical reliability by reducing hallucinations through grounding, implementing HIPAA-aligned privacy controls (Cloud DLP, VPC Service Controls), and running structured evaluations with clinician feedback.”
Matt Shade
Director-level AI engineering leader specializing in media platforms and newsroom workflows
“Product-minded frontend/UX leader with notable experience at CNBC, where they owned premium subscription and engagement flows from problem definition through production and post-launch iteration. They stand out for combining high-polish React implementation, Figma-driven systems thinking, and AI-assisted prototyping to quickly ship and refine conversion-focused experiences.”
Ashi Sinha
Junior Software Engineer specializing in full-stack and ML/NLP systems
“Entry-level full-stack engineer with internship experience at Amazon (Appstore IAP flow + uninstall recommendation workflow) and a health-tech startup (OneVector) where they built a DSUR reporting workflow end-to-end, including document generation, S3-backed versioning/metadata, and secure preview/download. Demonstrates strong production debugging and reliability mindset (instrumentation, deterministic retrieval, idempotent writes) and focuses on UX/performance in high-stakes user flows.”
Chenghui Cai
Director of Applied Sciences specializing in reinforcement learning and agentic AI for finance
“Embodied AI/robotics ML engineer with hands-on experience deploying POMDP-based reinforcement learning controllers on real mobile robots and vehicle fleets. Strong in sim-to-real robustness (domain randomization) and production rollout practices (HIL, shadow-mode, canaries, safety instrumentation), and has published related work (mentions a NeurIPS paper).”
Durgaprasad G
Mid-level AI/ML Engineer specializing in LLM infrastructure, RAG, and agentic systems
“Stripe engineer who owned and unified multiple team RAG systems into a shared production platform used by 200+ internal operators, deployed on EKS with Kafka ingestion and hybrid retrieval. Drove measurable business outcomes including <400ms latency, ~35% inference cost reduction, ~25% accuracy lift via fine-tuning, and real-time auto-approval of 80%+ merchant compliance applications through strong observability and reliability patterns.”
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and real-time recommendation systems
Mid-level Data Scientist/ML Engineer specializing in LLMs, NLP, and recommender systems
Mid-level AI/ML Engineer specializing in cloud MLOps and GenAI for fraud detection
Senior AI Engineer specializing in healthcare and FinTech AI systems
Intern AI/ML Engineer specializing in LLM systems and cloud-native microservices
Senior AI/ML Software Engineer specializing in LLM and RAG systems
Junior AI/ML Engineer specializing in LLMs, RAG, and document intelligence