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Vetted Reinforcement Learning Professionals
Pre-screened and vetted.
Mid-Level Software Engineer specializing in cloud platforms, ML/GenAI, and distributed systems
Senior Data Scientist specializing in AI/ML platforms for finance and healthcare
Director-level Wireless Software Engineer specializing in 3GPP NAS protocol stacks (LTE/NR)
Mid-level AI/ML Engineer specializing in GenAI agents and production ML systems
Mid-level Machine Learning Engineer specializing in MLOps and Generative AI
Mid-level Data Scientist/ML Engineer specializing in LLMs, NLP, and recommender systems
Mid-level AI/ML Engineer specializing in LLM, RAG, and multimodal systems
Mid AI/Machine Learning Engineer specializing in LLMs, NLP, and Computer Vision
Staff Platform/ML Engineer specializing in agentic AI, RAG, and cloud infrastructure
Mid-level Machine Learning Engineer specializing in Bayesian inference and reinforcement learning
Mid-level Machine Learning Engineer specializing in fraud detection and recommendations
Mid-level Data Scientist specializing in FinTech and product analytics
Senior AI Engineer specializing in healthcare and FinTech AI systems
Aaditya Voruganti
Junior AI & Software Engineer specializing in robotics and ML infrastructure
“Robotics engineer from UIUC’s Intelligent Motion Lab who led the perception stack for a humanoid robotic nurse, fusing camera/LiDAR/IMU on NVIDIA Jetson Orin for real-time localization and scene understanding across six robots. Deep expertise in ROS 2 and edge ML optimization (TensorRT, CUDA, zero-copy), delivering major latency/throughput gains (10 FPS to 22+ FPS) and building fault-tolerant pipelines with gRPC offloading and real-time reliability practices.”
Balpreet Kaur
Junior Machine Learning Engineer specializing in LLMs and data pipelines
“Research Extern at Google DeepMind and former AWS Software Development Engineer Intern with a strong focus on practical, trustworthy AI engineering. Built a multi-agent RAG system for personalized news headline generation using a fine-tuned Flan-T5 model, parallel critic agents, FAISS retrieval, and style embeddings, while also leading a 3-person team on the project.”
Suraj Botcha
Intern AI/ML Engineer specializing in LLM systems and industrial AI
“Full-stack AI engineer who has built both document-intelligence products and agentic investigation systems end to end. At ControlRooms.AI, they helped ship a production-facing root cause investigation workflow for industrial operations using Neo4j, FastMCP, RAG, OCR/VLM inputs, and multiple LLMs, contributing to roughly a 10x reduction in manual investigation time. They stand out for designing explainable, traceable AI systems that surface evidence, uncertainty, and missing context rather than forcing overconfident answers.”
Jacqueline Zhang
Mid-level Machine Learning Engineer specializing in LLMs, fairness, and healthcare ML
“ML/NLP practitioner with a master’s thesis focused on domain-adaptive knowledge distillation for LLMs (LLaMA2/sheared LLaMA), showing improved perplexity and ROUGE-L on biomedical data. Also built real-world data linking and search systems: integrated ClinicalTrials.gov with FAERS using fuzzy matching + embeddings, and delivered an LLM-powered FAQ recommender at Hyperledger using sentence-transformers, FAISS, and fine-tuning to mitigate embedding drift.”
Likhitha Bethi
Mid-level Software Engineer specializing in backend systems, distributed systems, and applied AI
“Goldman Sachs engineer who owned end-to-end features for an internal onboarding and case management platform, spanning React/TypeScript UI, a GraphQL gateway, and Node + Spring WebFlux microservices. Built and operated a Kafka-based ingestion and search pipeline with DLQs, retries, idempotency, and strong observability, and improved developer experience via backward-compatible GraphQL API design and schema-driven documentation.”
Shweta Chavan
Junior Computer Vision & ML Engineer specializing in autonomous perception systems
“LLM/RAG engineer who built a production-style multi-agent orchestrator for resume-to-recommendation workflows (PDF ingestion through screening and recommendations), emphasizing prompt tuning and strict JSON output contracts. Currently building a RAG application for an NGO using Airflow (DAGs + embeddings) and tackling messy, missing/imbalanced data; has hands-on retrieval stack experience (FAISS/HNSW, bge embeddings) and uses rigorous evaluation metrics for groundedness and hallucination control.”
Chinmayee Wamorkar
Mid-level Robotics & Autonomy Engineer specializing in MPC, RL, and GPU-accelerated optimization
“Robotics software engineer from Ati Motors who brought a Linear MPC approach (based on Kuhne et al.) into production, rebuilding parts of the planning stack to eliminate oscillations and safely double AMR speed from 0.8 m/s to 1.6 m/s. Also delivered an end-to-end point-cloud detection pipeline (PointPillars) including synthetic data generation in Isaac Sim and TensorRT deployment for real-time human/trolley detection, with a strong focus on production reliability via iterative hardening and nightly SIL.”
Muhan Zhang
Junior AI Software Engineer specializing in LLM pipelines, OCR, and RAG
“Built and shipped a production LLM pipeline for nursing home Medicare reimbursement (PDF OCR + fact extraction + keyword RAG + QA) that reportedly increased payouts by ~$1K/month per patient. Strong in LLM ops/benchmarking (ground truth, LLM-as-judge, cost/I-O tracking) and pragmatic optimization—swapped retrieval approaches, fine-tuned a small model to cut OCR cost 90%, and migrated workloads to Azure/Temporal to scale nightly processing 10x.”
Siddhik Reddy Kurapati
Junior Controls & Motion Planning Engineer specializing in MPC, RL, and autonomous systems
“Robotics researcher focused on learning-based navigation: builds sub-goal generation and cost-to-go models (Bayesian network-based) integrated with motion planning and MPC/NMPC control. Has hands-on ROS 2 package development across vehicles, drones, and manipulators, and uses a broad simulation stack (Isaac Sim, Gazebo, MuJoCo, PyBullet, PX4) to test and integrate systems.”
Gurnoor Kaur
Intern Robotics Software Engineer specializing in motion planning and robot perception
“Robotics software engineer with Amazon Robotics internship experience who built a visual-servoing architecture from scratch, navigating multiple simulator pivots to achieve a closed-loop motion-planning and execution prototype. Currently working with ROS 2 on a medical assistive feeding robot using the Kinova Kortex platform (MoveIt2, ros2_control, Gazebo/RViz), and has demonstrated strong real-time debugging and distributed-system synchronization using Carbon and Docker.”