Vetted OpenCV Professionals

“Applied ML to physical EV fleet systems at ST Labs, building a real-time CNN-LSTM fault prediction pipeline from streaming vehicle telemetry and addressing live data alignment issues via resampling/interpolation and buffered inference. Also developed a V2G/G2V energy transfer algorithm to automate charging/discharging for profit optimization, and made high-impact low-latency pipeline decisions at Astera Holdings using profiling, replay testing, and live A/B validation.”

“ML/AI engineer with production experience across retail and healthcare: built a real-time computer-vision shelf monitoring system at Walmart and optimized edge inference latency by ~30% using TensorRT/ONNX and pruning. Also partnered with CVS Health clinical/pharmacy teams to deliver a medication-adherence predictive model, using Streamlit explainability dashboards and achieving an 18% adherence improvement.”

“AI/ML engineer who has shipped production AI systems end-to-end, including an automated multi-channel (Gmail/WhatsApp/voice) candidate interviewing workflow and an enterprise RAG knowledge search platform. Demonstrates strong production rigor (monitoring, A/B tests, guardrails, schema validation, shadow testing) with quantified impact: ~60–70% reduction in interview evaluation time and ~20–30% relevance gains in RAG retrieval.”

“Full-stack engineer with startup ownership experience (Aiir) building 15+ TypeScript/Go microservice APIs on GCP Cloud Run with Kafka-based async event streaming and React CRM integrations for billing/analytics. Strong post-launch operator who tuned Oracle performance (partitioning/indexing/query optimization) and validated a 23% retrieval-time reduction via AWR, and has a quality/DevSecOps mindset (94% Pytest coverage, GitHub Actions, SonarQube, Twistlock, CloudWatch) including migrating 18+ production CI/CD pipelines.”

“GenAI/LLM engineer who architected and deployed a production RAG “research assistant” for JPMorgan Chase’s regulatory compliance team, focused on safety-critical behavior (mandatory citations, refusal when evidence is missing). Deep hands-on experience with LlamaIndex, Pinecone, Hugging Face embeddings, LangGraph agent workflows, and metric-driven evaluation (golden sets, TruLens), including a reported 28% relevancy lift via cross-encoder re-ranking.”

“Robotics software engineer with hands-on experience across reinforcement learning and ROS/ROS2, including a project teaching Boston Dynamics Spot to open a door by combining vision-based pose estimation with SAC-trained IK and a walking policy in MuJoCo. Previously built ROS Noetic control for surgical robots using RCM with MoveIt IK and achieved sub-0.02s latency via threading; also participated in a NASA ROS2 space simulation building rover teleop and sensor-driven mapping.”

“Software Engineer at Armstrong Robotics building multithreaded C++ perception/planning/control software for robotic arms running commercial dishwashers deployed across multiple restaurant sites (up to ~2,000 dishes/day per installation). Strong in production operations: on-call debugging with deep logging/video analysis, rapid hotfixes, Datadog-based monitoring, and a Three.js calibration tool plus large regression test suite to de-risk live deployments.”

“Robotics software engineer who built an end-to-end mobile manipulation platform (Franka Panda on a Clearpath Ridgeback) for a simulated-kitchen human-robot interaction study with natural speech commands, implemented in Python/ROS. Has hands-on experience integrating diverse sensors (RealSense, LiDAR, biosignals) with deep learning frameworks (PyTorch, Hugging Face) and fine-tuning GPT-Neo, plus simulation (Gazebo) and modern deployment practices (Docker/Kubernetes, CI/CD).”

“GenAI/ML engineer in Citigroup’s finance environment who has deployed production RAG systems for investment banking under strict privacy and model-risk constraints. Built an internal-VPC Llama2 + Pinecone + LangChain solution with NER redaction and citation-based verification to prevent hallucinations, delivering major time savings, and also partnered with global finance executives to ship an AI early-warning indicator for treasury/liquidity risk.”

“Robotics software engineer who owned the end-to-end navigation stack for a mobile manipulation robot (Cone-E), integrating ZED-2i SLAM into a real-time occupancy grid with live obstacle avoidance, A* planning, and lookahead control. Strong in real-time debugging and stability improvements (goal snapping/locking, obstacle persistence, rate-limited replanning) and validates changes on hardware, supported by simulation (Gazebo/Webots) and Docker/CI-based testing.”

“Interned at Rivian where they built and deployed a production Whisper-based ASR + LLM real-time event labeling pipeline to help autonomous-vehicle engineers diagnose failures and route issues to triage teams. Also built a stateful multi-agent "Code Partner" developer assistant using LangGraph/LangChain (planner/router/coder/critique/tester) with evaluation, adversarial testing, and stakeholder-friendly communication practices.”

“Computer-vision-focused candidate with internship experience at ASML (Silicon Valley) building object detection models (YOLO, RT-DETR) for SEM defect inspection. Worked end-to-end on preparing multi-resolution datasets and tuning/training strategies, noting improved performance on low-quality images when training jointly on higher-resolution data.”

“Open-source maintainer of KafkaJSUI, a Vue.js-based Kafka browser UI, focused on making large-topic exploration fast and responsive. Delivered major performance wins (incremental fetching, virtualized lists, WebSocket streaming, backpressure, Web Worker offloading) cutting load times to sub-200ms, and also strengthened CI and developer documentation while handling community-reported issues end-to-end.”

“AI/ML engineer at J.P. Morgan Chase who deployed a production financial-risk prediction platform combining CNN/LSTM/gradient boosting on AWS SageMaker, with automated drift-triggered retraining and governance-grade fairness testing. Leveraged SageMaker Clarify plus SMOTE and LLM-generated synthetic data to improve minority-group F1 by 0.12, and communicated results to non-technical risk/ops teams via Power BI dashboards.”

“ML Engineer with recent Goldman Sachs experience building and deploying a production RAG/LLM assistant for summarization, drafting, and internal knowledge retrieval across financial, risk, and compliance documents. Designed for heavy regulatory constraints and scaled to 10,000+ concurrent users using Kubernetes-based orchestration, dynamic LLM routing, and rigorous testing (adversarial prompts, A/B tests, load simulations) with privacy controls like differential privacy.”

“Robotics software engineer focused on state estimation and localization reliability, with deep hands-on EKF tuning/validation using DGPS ground truth and integrity-risk-based uncertainty calibration. Built middleware-agnostic interfaces with ROS wrappers to enable repeatable ROS bag playback testing, and implemented CI at Caterpillar to automatically build the localization stack and run unit tests plus bag-based regressions before merge.”

“Full-stack engineer with fintech/trading domain experience (Fidelity) and startup SaaS CRM/billing platform work (Zoho), building real-time portfolio analytics and trade-processing systems. Strong in microservices, event-driven architectures (Kafka/WebSockets), and AWS/Kubernetes operations with measurable performance gains (~34–35% latency reduction) and maintainability improvements (~40% faster deployments). Targeting a founding full-stack engineer role in NYC with meaningful equity.”