Vetted Deep Learning Professionals

“HR Business Partner with GroupM experience supporting multiple mergers, building and transitioning Analytics/Programmatic/Account Management teams through hands-on job architecture, benchmarking, and performance management. Uses HR analytics (including an Excel-based retention model with visual reporting) and cross-functional partnership with IT to reduce disruption and improve retention during restructuring and major policy changes like return-to-office.”

“Robotics software engineer who built a ROS Noetic-based perception-to-control stack for a pick-and-place robotic arm, integrating OpenCV/TensorFlow vision with motion planning and PID tuning. Demonstrated strong real-time debugging skills (rosbag, queue/latency fixes) and experience deploying reproducible robotics environments with Gazebo simulation, Docker, and GitLab CI.”

“Backend engineer who built and migrated a large-scale document intelligence platform used by legal, healthcare, and insurance clients, processing millions of pages. Experienced moving from a monolithic, LLM-heavy approach to a modular FastAPI service architecture with ML classification + RAG, strong validation/auditability, and enterprise security (JWT/OAuth, RBAC, PostgreSQL RLS) with zero-downtime incremental rollouts.”

“Robotics software engineer with internship experience at Lucid Motors building a mapping/localization pipeline that fuses LiDAR, camera, and GPS into high-fidelity 3D maps for autonomy/ADAS. Strong in SLAM and multi-robot systems—has modified ROS/ROS2 SLAM packages (FAST-LIO2, LIO-SAM) at the source level, optimized real-time multi-drone coordination for low-latency data sharing, and used Gazebo + Docker to simulate and deploy research robotics stacks.”

“Backend/platform engineer who has owned a Python/FastAPI results API and deployed it on Kubernetes with Helm and GitHub Actions-driven CI/CD. Demonstrates strong production operations mindset across performance tuning, monitoring, safe rollouts/rollbacks, and phased migrations, plus hands-on Kafka streaming experience focused on ordering and idempotency.”