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Vetted Reinforcement Learning Professionals
Pre-screened and vetted.
Apoorva Nanabolu
Senior Data Scientist / Generative AI Engineer specializing in fraud, risk, and MLOps
“Built and deployed a production LLM/RAG fraud investigation system to replace manual investigator workflows, combining transaction data, historical cases, and policy documents with agent-style steps and LoRA fine-tuning. Demonstrates strong reliability engineering (grounding, citations, abstention paths), performance optimization (retrieval/indexing/caching), and end-to-end MLOps orchestration using Azure ML Pipelines/MLflow plus Kubernetes/Argo with canary and rollback deployments.”
ZHIYONG JIANG
Senior AI & Machine Learning Engineer specializing in GenAI, Agentic AI, and RAG
“Built a production agentic AI system to automate data science work using a layered architecture (executive-summary handling, tool-based execution, and on-the-fly code generation). Demonstrates strong end-to-end agent development practices including RAG with vector databases, prompt engineering, and multi-method evaluation (LLM-as-judge/human/code-based), plus Airflow-based orchestration for ML data pipelines and close collaboration with business end users.”
Sohan Kshirsagar
Junior Software Engineer specializing in cybersecurity and cloud-native AI
“Backend-focused full-stack engineer who built an MVP at Neon AI for PhD students: a FastAPI backend integrating multiple cloud and local LLMs plus a RAG pipeline with session/identity management, designed to be modular and extensible across domains. Also has VMware experience debugging production issues and executing safe, API-compatible refactors with staged rollouts and strong security controls.”
Moh Abdullah
Senior AI/ML Engineer specializing in Generative AI, LLMs, and production ML systems
“ML/AI engineer with hands-on ownership of both classical ML and GenAI systems in production. They built an end-to-end churn prediction service on AWS and also shipped RAG-based document search/summarization features, with clear experience in monitoring, hallucination reduction, cost/latency optimization, and creating shared Python/LLM infrastructure used across teams.”
Mid-level AI Engineer specializing in Generative AI, LLMs, and RAG systems
Intern Computer Vision Engineer specializing in robotics perception and SLAM
Mid-level Quality Assurance Engineer specializing in test automation and reliability
Entry-level Instrumentation & Controls Engineer specializing in nuclear power systems
Intern Software & Data Engineer specializing in ML, robotics, and computer vision
Senior Simulation Engineer specializing in robotics and physics-based real-time simulation
Senior Software Engineer specializing in machine learning and backend microservices
Entry-level Robotics & Software Engineer specializing in automation, LLMs, and multi-robot systems
Intern Software Engineer specializing in machine learning and cloud systems
Intern Software Engineer specializing in full-stack and machine learning
Intern Robotics Engineer specializing in autonomous systems and control
Junior AI Engineer specializing in LLM systems and AI for drug discovery
Mid-level AI/ML Engineer specializing in recommender systems, MLOps, and Generative AI
Mid-level AI/ML Engineer specializing in LLMs and document intelligence
Mid-level AI/ML Engineer specializing in MLOps, NLP, and predictive modeling