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Vetted Recommender Systems Professionals
Pre-screened and vetted.
David Kidwell
Senior AI/ML Data Scientist specializing in NLP, computer vision, and MLOps
“Applied LLMs and a graph-RAG architecture in Neo4j to automate an accounting firm's cross-checking of transactional books against tax regulations, indexing 1,000+ pages into a knowledge graph with vector search. Combines agentic LLM workflows with classical NER (Hugging Face/NLTK) and validates using expert-labeled held-out data plus precision/recall and measured accountant time savings after deployment.”
Ravi Subramanian
Executive Technology Leader specializing in B2C marketplaces, cloud platforms, and AI products
“20-year technology builder with ~8 years in healthcare AI, currently at Keycentrix modernizing a legacy pharmacy solutions business. Shipped an OCR MVP within days and delivered a rebate-based product generating ~$50K/month, leveraging Claude/LangGraph agentic automation to replace work typically requiring a much larger engineering team. Developing a "Longevity AI Copilot" B2B platform that synthesizes research, labs, and wearable data into personalized longevity protocols for HNW and corporate wellness markets; concept validated but not yet incorporated or funded.”
Nate Zaidi
Senior Full-Stack Engineer specializing in Python, AI/ML, and cloud applications
“Backend/data engineer with hands-on production experience across FastAPI/PostgreSQL APIs and AWS (Lambda, ECS) delivered via Terraform + GitHub Actions. Built Glue-based ETL pipelines into Redshift with schema evolution and data quality checks, modernized legacy reporting into Python microservices, and has demonstrated measurable SQL performance wins (multi-second query reduced to sub-300ms).”
Staff Machine Learning Engineer specializing in LLM agents and ML systems
Senior Data Scientist specializing in GenAI, LLM systems, and production ML
Intern Software Engineer specializing in full-stack and distributed systems
Mid-level Data Scientist specializing in machine learning, analytics, and cloud data pipelines
Mid-level AI/ML Engineer specializing in NLP, Generative AI, and fraud detection
Mid-level Machine Learning & Generative AI Engineer specializing in enterprise RAG and MLOps
Junior Data Analyst / ML Engineer specializing in analytics pipelines and recommendation systems
Staff Software Engineer/Manager specializing in Generative AI and enterprise platforms
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and document intelligence
Mid-level Machine Learning Engineer specializing in NLP, recommender systems, and MLOps
Mid-level AI Engineer specializing in LLM agents and applied machine learning
Mid-level Software Developer specializing in cloud microservices and full-stack platforms
Senior Machine Learning Engineer specializing in Generative AI and NLP
Mid-level Mechanical Engineer specializing in medical robotics and machine learning
Sanya Dod
Junior Software Engineer specializing in AI/ML and verification
“Embedded/real-time robotics-style engineer with hands-on STM32 development, sensor integration, and low-level drivers, focused on deterministic control behavior. Demonstrated systematic debugging of jitter/latency by instrumenting the sensing-to-actuation pipeline and eliminating blocking via interrupts, hardware timers, and DMA; also designs asynchronous, message-based interfaces for distributed real-time components. Familiar with ROS/ROS2 concepts (nodes/topics/callbacks) though not yet deployed a full production ROS system.”
Caden Cheah
Intern Full-Stack/ML Engineer specializing in LLM applications and mobile development
“Backend engineer who built a serverless AWS Lambda microservices backend for a parenting assistance mobile app, including a personalized recommendation system optimized to sub-500ms via precomputed scoring and DynamoDB caching. Demonstrates strong production pragmatism: CloudWatch-driven performance tuning (provisioned concurrency), zero-downtime phased schema migrations, and robustness patterns like optimistic locking and request deduplication. Also led a refactor of an LLM RAG pipeline to improve retrieval quality and cut latency from ~5s to ~3s.”