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Vetted AI & Machine Learning Professionals in the NYC Metro
Pre-screened and vetted in the NYC Metro.
Senior Machine Learning Engineer specializing in recommender systems, search, and NLP/GenAI
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
Mid-level Data Scientist / GenAI & ML Engineer specializing in LLM apps and MLOps
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.”
Alexander Choy
Director of AI/ML Engineering specializing in MLOps, data platforms, and 3D computer vision
“Backend/data engineer focused on production ML/LLM systems: built a real-time FastAPI inference API on Kubernetes with strong reliability patterns (timeouts, idempotent retries, centralized error handling). Delivered AWS platforms using EKS + Lambda with GitHub Actions/Helm CI/CD and built Glue-based ETL from S3/Kafka into Snowflake with schema evolution and data-quality controls; also modernized legacy analytics/recommendation workflows into Python services with safe, feature-flagged cutovers.”
Yue Su
Junior AI/ML Engineer specializing in LLM agents, RAG, and distributed systems
“Python backend engineer focused on high-throughput document/PDF processing systems, building end-to-end pipelines that extract structured content for downstream NLP use cases. Demonstrates strong practical MLOps-adjacent infrastructure skills: Kubernetes deployments, GitLab CI, GitOps workflows, and an incremental migration to AWS using EC2/Lambda tradeoffs. Deep hands-on optimization experience (selective OCR, layout-aware extraction, parallelism, caching, idempotency, and backpressure/autoscaling).”
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.”
Benjamin Ung
Senior Machine Learning Software Engineer specializing in computer vision and simulation
“Robotics engineer who worked on a lunar rover program, building a simulation environment that mirrored real hardware interfaces and incorporated moon-terrain slip/friction modeling validated against a physical “moon yard.” Also integrated an ML-based munition X-ray inspection system via REST APIs, deploying and scaling inference on Azure with Kubernetes plus Prometheus monitoring, load balancing, and self-healing reliability mechanisms.”
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.”
Senior AI/ML Engineer specializing in conversational AI and full-stack systems
Mid-level Data Scientist/ML Engineer specializing in LLMs, NLP, and recommender systems