Vetted Reinforcement Learning Professionals

“Robotics software engineer (master’s student) who placed 3rd in the CMU VLA challenge and presented at IROS, building an LLM-powered language system (Gemini 2.5) for mobile-robot scene Q&A and language-based navigation. Hands-on ROS1/ROS2 experience including ros2_control + PILZ planning for a KUKA arm, plus simulation (Gazebo) and containerized submissions with Docker.”

“Machine learning/NLP practitioner at J.P. Morgan who led development of a production RAG system and an entity resolution pipeline for complex financial data. Deep hands-on experience with embeddings (Sentence-BERT), vector search (FAISS/pgvector), LLM fine-tuning (LoRA/PEFT), and rigorous evaluation (human-in-the-loop + A/B testing) backed by strong MLOps on AWS (Docker/Kubernetes, MLflow, Prometheus/Datadog).”

Kowshika M

Screened

Mid-level AI/ML Engineer specializing in LLM fine-tuning, inference optimization, and AI safety

Santa Clara, CA5y exp

“AI/LLM engineer with production experience at NVIDIA, where they fine-tuned and deployed a financial-services chatbot and cut latency ~50% using TensorRT + NVIDIA Triton, scaling via Docker/Kubernetes. Also has consulting experience at Accenture delivering a predictive maintenance solution for a logistics network, bridging non-technical stakeholders with actionable dashboards.”

Prahlad Vivek

Screened

Intern Robotics Engineer specializing in robot learning, SLAM, and control

Wilton, CT3y exp

“Robotics architect intern/new-grad focused on warehouse AMRs, building ROS2 sensor-fusion and SLAM stacks (FastSLAM-style particle filter) and validating in Gazebo with ground-truth metrics. Also interned at ASML debugging real-time in-vacuum robot behavior via Python state-machine telemetry scripts, identifying a firmware driver issue impacting throughput.”

“ML/Simulation engineer who productionized a Multi-Agent Reinforcement Learning system for 30+ firms at Belt and Road Big Data Company, integrating research code into an enterprise backend via Dockerized deployment and scalable data pipelines on GCP/Vertex AI. Demonstrated strong production debugging by tracing apparent network timeouts to hardware memory exhaustion caused by software state-history garbage collection issues, and built custom reward functions to model complex market dynamics (entry/exit, pricing).”

Het Maheshkumar Sekhalia

Screened

Entry-level Robotics Researcher specializing in autonomy, motion planning, and control

Pittsburgh, PA1y exp

“Robotics software engineer focused on simulation-first autonomy and learning: implemented TD3 and CLIP-guided pretraining for physics-based humanoid skill learning in Isaac Gym/DeepMimic. Also built a ROS2 + dual-Docker closed-loop stack for an autonomous wheel loader in Isaac Sim, combining global planning, B-spline smoothing, and real-time NMPC control.”

Krishna Reddy

Screened

Mid-level AI/ML Engineer specializing in fraud detection and clinical LLM assistants

New York, NY6y exp

“Built and deployed a production clinical support LLM assistant at Mayo Clinic using a LangChain-orchestrated RAG architecture (Llama 2/PaLM) over de-identified clinical records, integrating BigQuery with Pinecone for semantic retrieval. Focused on healthcare-critical reliability by reducing hallucinations through grounding, implementing HIPAA-aligned privacy controls (Cloud DLP, VPC Service Controls), and running structured evaluations with clinician feedback.”

“AWS AI Lab engineer who deployed a production Chain-of-Thought analytical agent for tabular reasoning, emphasizing grounded tool-constrained workflows with schema-validated intermediate outputs. Built robust evaluation/logging with step-level observability to catch regressions across model versions, and has experience scaling distributed LLM training via Slurm + DeepSpeed/FSDP with checkpointing and failure recovery.”

“Built and deployed a production RAG-based semantic search and summarization system for large legal/technical document sets, owning the full backend (embeddings, vector store, chunking, prompting) and driving a reported 40–60% reduction in manual review time. Experienced with LangChain/LlamaIndex plus Airflow/Temporal-style orchestration, and applies rigorous evaluation/monitoring (A/B tests, drift detection, staged rollouts) to keep agentic systems reliable. Also partnered with a supply-chain manager at TE Connectivity to deliver an AI inventory recommendation tool projected to drive millions in value.”

Dexin Huang

Screened

Junior AI Engineer specializing in LLM systems, RAG, and full-stack automation

Guilford, CT1y exp

“Built and deployed an AI receptionist product for field-service businesses (HVAC/electrician), including real-time Jobber scheduling integrations and Twilio-based calling. Combines hands-on customer/operator shadowing with strong production engineering (queueing to handle API limits, rigorous testing/mocking, mirrored prod environment) and cross-layer troubleshooting, driving user adoption through review/override workflows.”

“Robotics software engineer with Stanford coursework and Georgia Tech research experience, focused on end-to-end autonomy for mobile manipulation and real-time planning under uncertainty. Built a ROS 2 LoCoBot system combining Gemini speech-to-text, YOLO-based RGB-D perception, navigation, and grasping with robust synchronization/TF fixes, and developed an information-theoretic UGV planner for radiological source localization validated via Monte Carlo simulation.”