Vetted C++ Professionals

“Game developer/team lead on the FPS zombie project "Dreadful" who wore many hats (project management, systems/level/UI design, AI assist, QA) and iterated heavily via playtests and heatmap data. Built and tuned UE5 Blueprint-based crowd-control/gravity abilities with cooldowns and actor filtering, and made scope-smart feature swaps by reusing existing systems to hit deadlines.”

“Full-stack engineer who built and supported production features in a productivity/task-tracking app using Next.js App Router + TypeScript (server components for initial render, client components for interactivity, API route handlers for mutations). Also designed and optimized Postgres data models/queries and implemented resilient, event-driven payment processing with idempotency, retries, audit logs, and strong testing/observability practices.”

“Full-stack engineer with fintech trading-platform experience who shipped and operated a real-time portfolio P&L/performance feature end-to-end (React + Node/WebSockets + MongoDB) on AWS, including significant performance tuning under peak trading load. Also built a Spark-based trading analytics pipeline with idempotency and reconciliation for auditability, and has a personal React/TS + Node/Express project (Artsy) with JWT auth and schema-evolution practices.”

“Backend/AI engineer who has built and shipped two products: Know Founder (Python/SQL/AWS) scaling to 2,000+ users in the first month, and Unifr (unifr.online), an AI search visibility engine that queries multiple LLMs and turns responses into structured brand insights. Strong in production reliability/performance (Redis caching, indexing, precomputation) and in designing agentic workflows with guardrails, validation, retries, and human escalation.”

“Game developer with 13 years in the games industry who built a Unity VR feature using machine learning to recognize player-drawn spell symbols, training an accurate neural network from an existing dataset and solving tricky screen-space/world-space input constraints. Actively uses AI assistants for targeted code snippets and code review while emphasizing understanding over "vibe coding," and is interested in expanding into multiplayer/networking.”

“Master’s CS candidate with backend internship experience modernizing live operational workflows at NatWest/NetWess, focusing on reliability improvements, safer CI/CD deployments, and incremental refactors using feature flags and rollback paths. Built FastAPI-based APIs with strong security patterns (JWT + 2FA/TOTP, centralized authorization, RLS) and demonstrated attention to edge cases like idempotency and data consistency in a Netflix-clone project.”

“Robotics software engineer with 2+ years across ROS1/ROS2 projects spanning humanoid behavior engines and agricultural robots. Built an LLM-driven, ROS2-lifecycle-based decision system plus micro-ROS firmware on Teensy for modular sensors/motors, adding health monitoring that improved reliability 10x. Strong simulation/testing and deployment discipline (Gazebo, 95% coverage, Docker + AWS Greengrass/ECR, CI/CD) and demonstrated localization expertise with EKF sensor fusion achieving <0.5% error.”

“Robotics software engineer in an Autonomous Vehicle Lab building an end-to-end ROS 2 autonomous golf cart stack (sensor integration, SLAM, planning, and camera+LiDAR perception). Demonstrated strong systems-level debugging by fixing a FastLIO2 LiDAR timestamp/IMU-window issue that restored mapping quality, and stabilized real-time GigE camera perception by diagnosing backpressure and tuning ROS 2 QoS plus compressed transport.”

“Insurtech R&D leader turned CTO with 25+ years across telecom, DoD, automotive, and commercial insurance. Built patented AI-driven insurance document ingestion (>95% accuracy with 1–3 samples) and led Azure-based backend/service development while managing up to 15 engineers at a commercial P&C MGA that reached a ~$60M book of business (~30 loss ratio) before run-off due to paper issues. Known for hands-on discovery (including field inspections) and rapid MVP delivery, including a risk-inspection iPad app that auto-generated draft reports.”

“Embedded robotics software engineer specializing in C/C++ on ARM controllers, building sensor/actuator drivers and stabilizing real-time control loops under load using RTOS profiling and priority tuning. Strong ROS 2 integrator across microcontrollers and embedded Linux, with hands-on HIL testing (timing fixes, automated fault injection) and distributed robot comms via DDS, plus simulation (Gazebo/Webots) and Docker/CI/CD for reliable deployment.”

“Robotics software engineer with hands-on AGV/AMR experience at ERIC Robotics, building ROS2-based LiDAR perception and localization on NVIDIA Jetson for real-time deployment. Improved unstable localization in challenging environments (e.g., tunnels/bushes along rail tracks) via scan-matching, filtering, and consistency checks, and cut latency by moving from rclpy to rclcpp and leveraging CUDA. Comfortable across the stack from simulation (MuJoCo/Isaac Sim/Gazebo, domain randomization) to deployment tooling (Docker, basic CI) and distributed ROS2/DDS systems.”

“Robotics software engineer focused on ROS 2 localization/SLAM: built a particle-filter (Monte Carlo) localization system in Python with likelihood-field modeling to handle noisy LiDAR and dynamic environments. Strong in debugging ROS 2 integration issues (tf2 frame sync, DDS/QoS message reliability) and in profiling/optimizing pipelines to reach real-time performance (~10 Hz) using precomputation and KD-trees.”

“Built and led an autonomous driving software effort for Formula Student, owning the full autonomy stack (perception, planning, control) orchestrated in ROS. Implemented stereo depth + YOLO object detection, RRT/RRT* planning, and a robust SLAM pipeline (Kalman filter, submapping) while leveraging Gazebo simulation and modern deployment tooling (Docker/Kubernetes, AWS, GitHub Actions CI/CD).”

“Robotics engineer with Honeywell and Tata Motors experience deploying ROS/ROS2 autonomous mobile robot fleets into live factory environments, integrating sensors, safety PLCs, and on-prem services. Known for solving end-to-end latency and stability issues (including network spikes under load) using gRPC, Docker, and improved diagnostics—cutting diagnosis time from hours to minutes and achieving sub-150 ms control response.”

“Built and deployed a fine-tuned Qwen 2.5 14B model into production at Dextr.ai as the backbone for hotel-operations agentic workflows, running on AWS EKS with Triton and TensorRT-LLM. Demonstrates strong cost-aware LLM engineering (QLoRA, FP8/BF16 on H100) plus rigorous benchmarking/observability (Prometheus, LangSmith) with reported sub-30ms TTNT. Previously handled long-running ETL orchestration with Airflow at GE Healthcare and Lowe's.”

“Robotics perception engineer focused on autonomous driving 3D detection, integrating PETR embeddings into BEVFormer and tackling hard orientation/temporal alignment issues in multi-camera BEV pipelines. Uses Gazebo with custom sensor plugins to validate calibration, timing, and transforms, and blends synthetic labels with real imagery for scalable 3D box generation.”

“Unity/VR developer and PhD researcher who served as lead developer on a clinical VR application (VheaRts) used by surgeons at Great Ormond Street Hospital, taking over a messy codebase and rebuilding its architecture, documentation, and extensibility. Also built research-focused Unity VR experiments (e.g., change blindness in a dynamic living room) with sophisticated logic to control participant attention while monitoring physiological cues.”

“LLM/RAG engineer with production deployments in highly regulated domains (Frost Bank and GE Healthcare). Built secure, explainable document-grounded Q&A systems using LoRA fine-tuning, strict RAG with confidence thresholds, and citation-based responses; also established evaluation/monitoring (golden QA sets, hallucination tracking, drift) and achieved ~40% latency reduction through retrieval/prompt tuning.”

“Full-stack engineer who has owned customer-critical analytics and course intelligence platforms end-to-end (React/TypeScript + Node/Express + SQL), including an internal self-serve Reporting & Analytics Center adopted by 1,000+ users. Demonstrates strong systems thinking across performance (2× faster heavy reports), reliability (feature flags, testing), and distributed architecture (RabbitMQ microservices with idempotency, DLQs, and correlation-ID observability).”

“Support engineer with IBM DFSMS OAM experience who restored a production TS7770 environment during a TS7760→TS7700 migration by using logs, SLIP traps, and dump analysis to pinpoint an SMS configuration (SCDS) issue, then partnering with the customer to redo the migration successfully. Also built a personal agentic news selector system and emphasizes documentation improvements and customer education to prevent recurring incidents.”

“Backend engineer with hands-on experience building secure Python/Flask services (sessions, JWT, RBAC) and optimizing PostgreSQL/SQLAlchemy performance, including custom SQL using CTEs/window functions profiled via EXPLAIN ANALYZE. Also integrates LLM features via OpenAI/Azure into backend systems and improves scalability with RabbitMQ-driven async processing, caching, and multi-tenant data isolation patterns.”

“AI engineer who deployed a production LLM-powered safety system for an education platform, combining rule-based checks, multi-LLM verification, and selective context (prompt+image vs image-only) to prevent explicit prompts/images from getting through. Strong focus on reliability via benchmarking, trace-based failure analysis, and continuous improvement driven by stakeholder feedback and manual review.”

“Built and iterated an end-to-end virtual waiting room for a real-time ticketing prototype, making concrete architecture tradeoffs (polling + Redis Pub/Sub) and improving performance post-launch with Redis caching (+30% throughput, -15% p99 latency). Also has hands-on experience building Spark/HDFS ETL pipelines with strong reliability/observability patterns and running disciplined NLP model evaluation loops on review-rating classification.”

“Built an AI basketball shooting coach that analyzes player form against NBA players and recruited 30+ beta users via Reddit to drive iterative UI/workflow improvements. Also has internship experience building an administrative server and coordinating API/database compatibility with another client server, emphasizing communication and integration quality.”