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Vetted Reinforcement Learning Professionals
Pre-screened and vetted.
Mid-level Full-Stack Java Engineer specializing in scalable microservices and real-time data systems
Mid-level AI/ML Engineer specializing in NLP/LLMs and production ML systems
Junior Software Development Engineer specializing in AWS cloud services and SDN
Junior Mechanical/Robotics Engineer specializing in controls, vehicle dynamics, and autonomy
Mid-level Machine Learning Engineer specializing in LLMs and RAG systems
Mid-level Machine Learning Engineer specializing in NLP, recommender systems, and on-device ML
Executive Enterprise Architect & AI/Cloud Engineering Leader (Oracle ERP, OCI, SRE)
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multi-agent systems
Mid-level Robotics & Computer Vision Engineer specializing in humanoid manipulation and RL
Senior Robotics Research Scientist specializing in safe, communicative motion planning and ML
Mid-level Software Engineer and Data Scientist specializing in scalable data systems
Mid-level Computer Vision Engineer specializing in robotics perception and mapping
Junior Robotics Engineer specializing in autonomous systems and robot learning
Junior AI Software Engineer specializing in LLM systems and retrieval (RAG)
Mid-level AI/ML Engineer specializing in LLMs, ranking systems, and MLOps
Principal Data Scientist / AI Engineer specializing in healthcare-native AI platforms
Rhutwij Tulankar
Engineering Manager and ML/Data Architect specializing in scalable data platforms and personalization
“Hands-on engineering manager at a marketing company leading a highly senior, distributed team (10 direct reports) while personally coding ~60–70% and owning end-to-end architecture across three interconnected products. Built agentic CRM automation and a reinforcement-learning-driven distribution layer for channel spend/bidding, with a strong focus on scalable design and observability (Prometheus/APM/logging) enabling frequent releases and few production incidents.”
Ashish Ernest Jeldi
Senior Data Scientist specializing in LLMs, agentic AI, and MLOps
“Built and shipped a production agentic LLM tool that helps internal teams update technical product whitepapers using plain-language edit requests, with strong guardrails (citations, verification, refusal/clarify flows) to reduce hallucinations and maintain compliance. Experienced taking LLM workflows from rapid LangChain prototypes to more predictable, debuggable LangGraph agent graphs, and orchestrating end-to-end ingestion/embedding/indexing/eval/deploy pipelines with Kubeflow.”
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.”