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Vetted Reinforcement Learning Professionals
Pre-screened and vetted.
Senior Software Engineer specializing in AI-driven workflow automation
Mid-level Full-Stack Engineer specializing in AI, LLM/RAG platforms, and data systems
AI/ML Software Engineer specializing in LLM agents and distributed systems
Senior Robotics Software Engineer specializing in perception, sensor fusion, and SLAM
Mid-level Software Engineer specializing in AI, reinforcement learning, and robotics
Mid-level AI/ML Engineer specializing in MLOps, LLMs, and real-time AI systems
Senior Computer Vision Engineer specializing in medical imaging and MLOps
Mid-level Robotics Software Engineer specializing in ROS2 autonomy and SLAM
Junior Robotics Engineer specializing in reinforcement learning and robot manipulation
Mid-level Data Scientist & AI/ML Engineer specializing in GenAI and LLM-driven enterprise systems
Senior AI/ML Engineer specializing in LLMs, NLP, and production MLOps
Mid-level Machine Learning Engineer specializing in MLOps and healthcare analytics
Senior Data Scientist specializing in NLP, MLOps, and cloud ML platforms
David Akhihiero
Junior Robotics Researcher specializing in SLAM, localization, and multi-robot navigation
“Robotics software engineer with internship experience at Lucid Motors building a mapping/localization pipeline that fuses LiDAR, camera, and GPS into high-fidelity 3D maps for autonomy/ADAS. Strong in SLAM and multi-robot systems—has modified ROS/ROS2 SLAM packages (FAST-LIO2, LIO-SAM) at the source level, optimized real-time multi-drone coordination for low-latency data sharing, and used Gazebo + Docker to simulate and deploy research robotics stacks.”
Vamsidhar Vuddagiri
Mid-level AI/ML Engineer specializing in LLM fine-tuning, RAG, and MLOps
“Built an LLM-powered academic research assistant for a professor (LangChain + OpenAI + arXiv) focused on synthesizing papers quickly, with emphasis on reliability (ReAct prompting, citation verification) and cost control (caching). Has production MLOps/orchestration experience at Cisco and HCL Tech using Kubernetes, plus MLflow and GitHub Actions for lifecycle management and CI/CD.”
Ankita Mungalpara
Mid-level Data Scientist specializing in Generative AI and multimodal systems
“Recent J&J intern who built a conversational RAG agent and led a shift from a monolithic model to a modular RAG workflow, cutting response time from several days to under a second by tackling data fragmentation, context retention, and embedding/latency optimization. Also worked on a large (7B-parameter) multimodal VQA pipeline for healthcare research and stays current via NeurIPS/ICLR and open-source contributions.”
Rahul Karanam
Senior Computer Vision & Robotics Engineer specializing in perception and warehouse automation
“Robotics engineer with hands-on experience scaling a multi-vendor heterogeneous warehouse robot fleet, building a distributed “traffic manager” for collision avoidance and real-time rerouting using CBS/MAPF and DCOP-style negotiation. Strong real-time/safety-critical systems background (RTOS, deterministic lock-free multithreading) plus modern perception and simulation tooling (CNN-LSTM/transformers, CARLA/Isaac Sim, VIO/GTSAM, camera-IMU calibration). Startup-oriented and comfortable moving quickly from prototype to production.”
Anvesh Reddy Narra
Mid-level AI/ML Engineer specializing in Generative AI, RAG, and MLOps
“Built a secure, on-prem/private GPT assistant to replace manual SharePoint-style search across thousands of policies/SOPs/engineering docs, using a production RAG stack (LangChain/LangGraph, FAISS/Chroma, PyMuPDF+OCR, vLLM). Implemented layout-aware ingestion (including table-to-JSON) and a multi-agent retrieval/generation/verification workflow with strong observability and compliance guardrails, delivering ~70% reduction in search time.”