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Vetted Feature Engineering Professionals
Pre-screened and vetted.
Stephen Koo
Senior Software Engineer specializing in LLM infrastructure and AI inference platforms
“Google Workspace AI engineer who owned major AI assistant infrastructure end-to-end: React/TypeScript UI, a Node.js context-aggregation gateway on Cloud Run, and a Go inference layer on GKE serving Gemini. Built and productionized Gmail RAG + agentic workflows with rigorous evals and guardrails, and has a strong track record of measurable impact (latency, engagement, acceptance-rate lifts) and zero-incident migrations using feature flags/strangler patterns across multiple ML teams.”
Senior Machine Learning Engineer specializing in recommender systems, search, and NLP/GenAI
Senior Full-Stack Software Engineer specializing in cloud storage and developer tooling
Senior Machine Learning Engineer specializing in large-scale AI systems
Senior AI Engineer specializing in LLMs, RAG, and production ML systems
Senior Applied AI Engineer specializing in recommendation, search, and ML platforms
Staff Machine Learning Engineer specializing in search, ranking, and LLM systems
Senior AI/ML Engineer specializing in LLMs, recommendation systems, and ML platforms
Senior AI/ML Engineer specializing in LLM applications, RAG systems, and MLOps
Staff Machine Learning Scientist specializing in NLP, LLMs, and Generative AI
Staff Full-Stack Software Engineer specializing in cloud platforms and healthcare data pipelines
Senior Machine Learning Engineer specializing in Generative AI and NLP
Kevin Wang
Senior Software Engineer specializing in cloud infrastructure and distributed systems
“Amazon engineer focused on productionizing LLM-powered developer workflows, including code assistance, debugging automation, and internal AI tooling. Stands out for combining hands-on ML systems work with strong platform engineering, including an orchestration engine that reportedly saved about $10K/day and reduced a manual workflow from 12 hours to under a second.”
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.”
Sudheer Bommichetty
Mid-level Machine Learning Engineer specializing in NLP, MLOps, and Generative AI
“Built and deployed a production LLM conversational AI system at OpenAI supporting chat, summarization, and semantic search at 1M+ requests/day, driving major latency (40%) and accuracy (25%) improvements through Pinecone optimization and tighter RAG with re-ranking. Also has Amazon experience improving recommendation systems by translating ML metrics into business terms to boost CTR and conversions, with strong MLOps/orchestration depth (Airflow, MLflow, SageMaker, Kubeflow).”
Pradeep Nadella
Mid-level AI/ML Engineer specializing in LLM optimization and real-time fraud/risk modeling
“ML engineer with 5 years at Stripe building and productionizing real-time fraud detection at massive scale (3M+ transactions/day; $5B+ annual payment volume). Delivered measurable impact (22% accuracy lift, 18% loss reduction, +3–5% authorization rates) and has strong MLOps/orchestration experience (Docker, Kubernetes, Airflow, MLflow, CI/CD, monitoring/rollback) plus a structured approach to LLM agent/RAG evaluation.”
Gabriel Aguiar
Entry-Level Software Engineer specializing in AWS cloud infrastructure and distributed systems
“Robotics software engineer with hands-on ROS 2 experience who helped build an autonomous 5-DOF robotic arm that plays Backgammon, owning perception (OpenCV) and game-logic while adding robustness features like lighting tolerance and auto-calibration. Also worked on a Raspberry Pi/LiDAR car project, improving mapping accuracy through data-logged calibration and contributing to multi-robot collision-avoidance coordination via a server-based pub/sub system.”
Barbara Skrzypek
Senior Research Scientist specializing in physics, machine learning, and scientific computing
“Research-oriented ML engineer/scientist with deep experience applying generative models, adaptive optimization, and HPC infrastructure to complex physics analyses. Built reusable Python-based tools that replaced expensive Monte Carlo workflows, integrated across HTCondor/SLURM environments, and reduced analysis timelines by 2x while supporting broader team adoption and training.”
Muneendra M
Mid-level Software Engineer specializing in event-driven backend and AI-enabled systems
“Full-stack engineer at Stripe who owned a webhook monitoring and retry platform end-to-end, spanning backend services, React dashboards, and production operations. Stands out for combining strong distributed-systems judgment with product polish, including a reported 31% improvement in webhook delivery reliability and UI improvements that reduced support burden.”
Staff AI Full-Stack Engineer specializing in LLMs, multi-agent systems, and Voice AI
Senior Software Engineer specializing in Python AI/ML integration and experimentation pipelines