Vetted Reinforcement Learning Professionals

“ML engineer with hands-on experience taking a Gaussian Process Regression-based intelligent survey timing system from build to real-world deployment, including a 3-week RCT on 120 participants and measurable improvements (15% response rate, 23% data quality). Also served as a key technical resource at CData for customer-facing demos and debugging hundreds of production issues, bridging engineering with Sales and Customer Success.”

“AI/LLM engineer who has shipped production systems end-to-end, including a note-taking product (Notey) combining audio/image capture, ASR, summarization, and a semantic chat agent over past notes. Also has applied ML experience in healthcare, collaborating directly with doctors to validate an EEG seizure-detection pipeline, and uses Kubernetes to optimize GPU usage for LLM training.”

“AI automation builder who shipped an OpenAI-powered weekly "trending AI tools" WoW reporting system (65 categories) that reduced a 6–7 hour manual process to ~10 minutes at negligible API cost. Also building a RAG-based content creation prompt engine that turns PDFs into storyboards with fact-checking/traceback to source lines, plus experience with AWS deployment components (Lambda, ECR, App Runner, Bedrock, API Gateway) and GitHub Actions.”

“Robotics/AI engineer focused on multi-agent reinforcement learning for Crazyflie drones, enabling coordination via implicit motion-based communication and a stabilizing FSM layer; reported 98.5% sim and 92% real-world behavior-recognition accuracy. Also built a modular ROS 2 wall-following system (custom nodes/services/actions) and a Raspberry Pi + OpenCV stereo-vision walking robot, emphasizing rigorous logging, stress testing, and sim-to-real deployment.”

“Robotics software engineer who led an autonomous nursery management robot project at Auburn University, spanning RGB-D/IMU sensor fusion, SLAM navigation, and real-time ML for plant detection/quality assessment. Strong ROS1/ROS2 background (C++/Python) with deployment on NVIDIA Jetson, including profiling-driven optimization of YOLO segmentation for real-time behavior and multi-robot (UGV/UAV) communication using ROS2.”

“AI Engineer at DXC Technology who has shipped production LLM/NLP systems on AWS (SageMaker, FastAPI) and optimized them for real-time latency and unpredictable traffic using quantization, batching, and autoscaling. Strong MLOps and monitoring discipline (MLflow, CloudWatch, SageMaker Model Monitor) and proven business impact—delivered models with 92% predictive accuracy and cut enterprise decision-making time by 30% through close collaboration with product managers.”

Monthir Ali

Screened

Senior AI/ML Engineer specializing in LLMs, RAG, and VR/XR multimodal systems

Salt Lake City, UT8y exp

“PhD researcher (University of Utah) who built a production RAG-powered Virtual Reality Research Assistant to answer lab research questions with concrete citations. Implemented an end-to-end LangChain pipeline using PyPDFLoader, chunking strategies, OpenAI embeddings, and ChromaDB, with emphasis on grounding to reduce hallucinations and ensure research-grade accuracy. Collaborated closely with a non-technical PhD advisor to scope requirements, manage cost constraints, and demo iterative progress.”

Jonathan Grebe

Screened

Mid-level Robotics Software Engineer specializing in ROS, C++ and embedded Linux

Ottawa, Canada7y exp

“Robotics software lead at Icor who grew from intern to owning the end-to-end software lifecycle for a mobile manipulator platform deployed to 300+ customers globally. Deep hands-on ROS2/MoveIt2 and navigation-stack integration (URDF/TF, sensors, behavior engine) plus production infrastructure (CI/CD, OTA, field OS upgrades) and real-world performance tuning for motion planning in EOD multi-robot environments.”

“ML/AI engineer with strong healthcare insurance domain depth who has owned fraud detection and LLM claims products end-to-end in production. Stands out for combining modern MLOps and RAG architecture with measurable business impact, including millions in fraud savings, 40% faster analysis, and reusable platform tooling that accelerated multiple teams.”

“AI/ML engineer with hands-on experience building a healthcare-focused generative AI application end-to-end, from architecture and data design through deployment, monitoring, and iterative improvement. Particularly strong in multi-agent LLM systems, fine-tuning, and safety guardrails, with measurable impact including a 20% accuracy lift to 91% and 10% latency improvement in a nutrition recommendation pipeline.”

“ROS2/Python robotics engineer who led a 4-person team building a simulated multi-robot warehouse system (SLAM + NAV2 + centralized task allocation) in Gazebo Ignition, including a distance/priority-based controller that reduced task completion time by ~30%. Also has hands-on real-time debugging/tuning experience for both mobile robots and a MyCobot 600 Pro manipulator, plus simulation work in CARLA using RL (TD3) and Social-LSTM for pedestrian behavior modeling.”

“Research assistant who single-handedly built and integrated an indoor autonomous wheelchair system using NVIDIA Jetson Nano, LiDAR, and a stereo camera. Implemented a multi-sensor perception pipeline (OpenCV/PCL) with ROS-based modular nodes, TF frame management, and robust debugging via RViz/rosbag, plus simulation testing in Gazebo and Dockerized environments for portability.”

“Robotics software engineer who built a ROS/C++ workcell stack to automate coating wooden panels with a 6-DOF arm, including trajectory generation, MoveIt/OMPL planning, and a single launch/config setup that runs in both Gazebo and on real hardware. Strong in debugging real-world planning failures (e.g., intermittent aborted/no-plan regions) through logging, planner swaps, and collision/kinematics tuning, and in designing modular ROS/ROS2 systems with versioned interfaces and translation layers for heterogeneous robots.”

“Deployment engineer at a robotics startup who owned end-to-end field deployments in greenhouse environments, including integrating humanoid robots (XArm 6), tuning perception stacks for real-world lighting shifts, and coordinating rapid fixes with hardware/software teams. Experienced debugging complex robotics integrations (LiDAR + NVIDIA Jetson + ROS2 + networking) and hardening solutions by automating configuration at boot, while also working directly with customers and training operators for ongoing support.”

“ML/NLP engineer with banking domain experience who built a GenAI-powered fraud detection and risk intelligence system at Origin Bank, combining RAG (LangChain + FAISS), fine-tuned BERT NER, and GPT-4/Sentence-BERT embeddings. Delivered measurable impact (25% higher fraud detection accuracy, 40% less manual review) and emphasizes production-grade pipelines on AWS SageMaker/Airflow with strong data validation and scalable PySpark processing.”

Akshay Bharadwaj Kunigal Harish

Screened

Mid-level Machine Learning Engineer specializing in NLP, computer vision, and LLM systems

Boston, MA5y exp

“Built a production multi-agent cybersecurity defense simulator orchestrated with CrewAI, combining Red/Blue team LLM agents, a RAG runbook retriever, and an RL remediation agent trained via state-space simplification and reward shaping for rapid incident response. Also partnered with quant analysts and fund managers to deliver an automated trading and portfolio management system using statistical methods plus CNN/LSTM models, reporting up to 15% weekly ROI.”

Ankush Desai

Screened

Junior Robotics Engineer specializing in ROS, perception, and robotic manipulation

2y exp

“Robotics software engineer focused on ROS2 autonomy stacks, with hands-on work spanning semantic 3D SLAM, sensor fusion, and controller customization. Built an indoor GPS-denied semantic SLAM system (>95% accuracy) and extended Nav2’s MPPI controller with a custom C++ critic to keep an agricultural rover centered in crop rows, boosting CO2 laser weeding effectiveness by 40%. Strong in simulation-to-real workflows (Isaac Sim, Gazebo Ignition) and deployment automation (Docker on Jetson Orin NX, GitHub Actions CI/CD).”

Jaykumar Kotiya

Screened

Mid-level Machine Learning & AI Engineer specializing in Generative AI, NLP, and MLOps

Boston, MA6y exp

“Built and deployed production LLM systems for summarizing sensitive legal and financial documents, emphasizing GDPR-aligned privacy controls and scalable hybrid cloud architecture. Experienced with Kubernetes/Airflow orchestration and rigorous testing/monitoring practices, and has delivered measurable business impact (18% conversion lift) by translating AI outputs for non-technical marketing stakeholders.”

Vaishnavi M

Screened

Mid-level AI/ML Engineer specializing in MLOps and Generative AI

5y exp

“At Liberty Mutual, built a production underwriting decision assistant combining LLM reasoning with quantitative models and strong auditability. Implemented a claims-based response verification pipeline that cut hallucinations from 18% to 3% and materially improved user trust/validation scores. Experienced orchestrating ML/LLM workflows end-to-end with Airflow, Kubeflow Pipelines, and Jenkins, including SLA-focused pipeline hardening.”

Kunal Sanghvi

Screened

Mid-level Data Engineer specializing in AI, NLP, and LLM systems

USA3y exp

“Built and deployed a production AI customer support chatbot at Unique Design Inc. using FastAPI, AWS, Docker, and retrieval-based grounding on internal documents. Stands out for hands-on ownership across discovery, deployment, incident debugging, and post-launch iteration, with a strong focus on making LLM systems reliable and safe in real business workflows.”

Raunak Bandyopadhyay

Screened

Intern-level Full-Stack Engineer specializing in AI and web systems

Amherst, MA0y exp

“Backend-focused intern in telehealth who combined engineering, QA leadership, and emergency infrastructure ownership. Built a text chat feature and an OpenAI-powered therapy chatbot, then stepped up to rebuild AWS infrastructure after it was accidentally deleted, while also improving chat responsiveness and reporting a 30% engagement lift.”

keerthana bhukya

Screened

Junior Software Engineer specializing in AI/ML and cybersecurity

Stockton, CA2y exp

“Salesforce-focused engineer with hands-on depth across Sales Cloud, Service Cloud, Apex, LWC, and Aura. Stands out for owning end-to-end automation features, making thoughtful async architecture decisions to balance performance and reliability, and designing responsive Lightning interfaces that hold up under large data volumes.”

“Robotics simulation/localization engineer who built a lunar crater navigation stack in ROS/ROS2 and Gazebo, including custom localization/perception/planning packages. Demonstrated strong debugging skills by using tf2 frame analysis to fix camera-to-base_link alignment, cutting heading error from 75° to 0.48°, and handled large NASA lunar imagery (~4GB) by converting/downsampling data for Gazebo.”

“AI/LLM engineer with production experience building an LLM-powered investment recommendation system using RAG and chatbots, deployed via Docker/CI/CD and scaled on Kubernetes. Demonstrated measurable performance wins (sub-200ms latency) through QLoRA fine-tuning and TensorRT INT8/INT4 quantization, plus strong MLOps/orchestration background (Airflow ETL + scoring, MLflow monitoring) and stakeholder-facing delivery using demos and Tableau dashboards.”