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Vetted Reinforcement Learning Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in LLM fine-tuning, RAG, and computer vision
Mid-level Robotics Engineer specializing in ROS2 control, humanoids, and medical robotics
Intern AI/ML Engineer specializing in cloud-native RAG systems and scientific ML
Intern Machine Learning Engineer specializing in computer vision and multimodal tracking
Mid-level AI/ML Engineer specializing in financial risk, fraud analytics, and MLOps
Mid-level AI/ML Engineer specializing in NLP, MLOps, and fraud & risk analytics
Mid-level Software Engineer specializing in distributed systems and AI agents
Intern Software Engineer specializing in AI/ML and cloud data pipelines
Mid-Level Backend Engineer specializing in distributed microservices and FinTech systems
Mid-level Research Scientist specializing in reinforcement learning and safety-critical robotics
Mid-level Robotics Engineer specializing in autonomous navigation and SLAM
Senior GenAI Engineer specializing in enterprise LLM systems and RAG platforms
Mid-level Data Scientist / ML Engineer specializing in risk, fraud, NLP and recommender systems
Mid-Level Full-Stack Software Engineer specializing in cloud-native web applications
Entry-Level Robotics Engineer specializing in automation, control systems, and autonomy
Bharath Reddy Nallu
Mid-level Machine Learning Engineer specializing in NLP and scalable MLOps
“Data/ML engineer in financial services (Northern Trust) who built a production RAG-based LLM system to connect structured transaction/portfolio data with unstructured market and internal documents for risk teams. Strong in end-to-end pipelines (AWS Glue/Airflow/PySpark), entity resolution, and taking models from prototype to reliable daily production with performance tuning (LoRA + TensorRT) and monitoring.”
Sai Manikanta Kasireddy
Mid-level Machine Learning Engineer specializing in cloud-native GenAI and RAG systems
“Built and productionized an internal GenAI chatbot that makes company policy/SOP knowledge instantly searchable, using a secure RAG architecture on AWS (Bedrock/Titan embeddings/OpenSearch Serverless, Textract/Lambda/S3 ingestion, Claude 3 Sonnet). Demonstrates strong MLOps/orchestration experience (Airflow, Step Functions with Lambda/Glue/SageMaker) and a rigorous reliability approach (RAGAS metrics, A/B testing, citation validation, monitoring), including collaboration with compliance stakeholders via review dashboards.”
Senior Full-Stack Python & AI Engineer specializing in FinTech and real-time platforms
Mid-level AI/ML Engineer specializing in generative AI and MLOps
Srikanth Natarajan
Junior Robotics Engineer specializing in perception, SLAM, and reinforcement learning
“Robotics software engineer with hands-on ROS 2 experience across drones, mobile robots, and manipulators. Built an end-to-end visual SLAM + navigation stack on a real robot using RTAB-Map, and implemented ROS 2-based coordination between a mobile robot and manipulator for camera-triggered object pickup. Optimizes real-time behavior by moving performance-critical code to C++ and deploying TensorRT-compressed models.”