Vetted OpenCV Professionals

“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.”

“Robotics software engineer with internship experience at John Deere and AeroVironment, working across C++/Python stacks and ROS2-based systems. Drove a proof-of-concept migration from an x86/FPGA target to NVIDIA GPU solutions and helped turn a hackathon prototype into a production-ready, CI/CD-driven build-and-deploy workflow with comprehensive automated testing.”

“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.”

“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.”

“Interned at Backblaze building production AI systems for incident response and security operations, including an internal LLM-powered incident triage assistant that used Snowflake + RAG over historical tickets/postmortems and delivered results via Slack and a web UI. Emphasizes reliability (PII filtering, grounding, schema validation, fallbacks) and rigorous evaluation/observability (offline replay, partial rollouts, time-to-first-action metrics, Prometheus/Grafana).”

“ML/LLM engineer who built a production RAG system (GPT-4 + FAISS + FastAPI) to deliver fast, grounded answers from proprietary documents, optimizing for sub-200ms latency and high-concurrency scale. Strong MLOps/observability background: drift monitoring with Prometheus + Streamlit, automated retraining via Airflow, Kubernetes autoscaling, and MLflow-managed model lifecycle, plus inference cost reduction through quantization and structured pruning.”