Vetted Computer Vision Professionals

“University Rover Competition autonomous-systems lead who architects and debugs a full ROS 2 autonomy stack (Nav2, vSLAM, EKF fusion) and backs it with strong engineering hygiene (Docker + GitHub Actions CI running headless Gazebo and colcon tests). Also has industry-facing ROS 2 hardware integration experience, building a ros2_control plugin for a Unitree G1 arm using CycloneDDS and optimizing real-time behavior via QoS tuning.”

“Backend engineer with insurance/claims domain experience who modernized legacy claims processing systems to support AI-assisted claim review. Emphasizes production-ready API design in Python/FastAPI (schemas, async, caching, graceful degradation), strong observability with Prometheus, and layered security including JWT auth plus database row-level security (Supabase/Postgres).”

“Robotics software engineer with hands-on ROS 2 experience across drones, mobile robots, and manipulators. Built an end-to-end visual SLAM + navigation stack on a real robot using RTAB-Map, and implemented ROS 2-based coordination between a mobile robot and manipulator for camera-triggered object pickup. Optimizes real-time behavior by moving performance-critical code to C++ and deploying TensorRT-compressed models.”

“Robotics software engineer with deep ROS2 experience who built a modular autonomous robotics stack (perception/sensor fusion, localization+mapping, and planning). Led development of a LiDAR+camera fusion and multi-object tracking pipeline (PCL + YOLO + Kalman filtering) and debugged real-time SLAM/localization issues via QoS/timestamp synchronization, EKF tuning, and SLAM Toolbox parameter optimization using Gazebo/RViz and rosbag replay.”

“Internship project at Builder Market: built an end-to-end production multimodal LLM application that estimates renovation/replacement costs from appliance photos (CLIP embeddings) or text descriptions, combining fine-tuning with agentic RAG. Focused heavily on real-world performance constraints—latency and cost—using parallel agent workflows, model routing to smaller/open-source models, re-ranking, and retrieval chunking, and collaborated closely with CEO/co-founders to deliver the solution.”

“AI/LLM engineer who built a production RAG-based Text2SQL engine using Qdrant, including creating the underlying business/DB documentation, generating a test dataset, and designing detailed SQL-quality metrics for validation. Also partnered with non-technical stakeholders on a speech recognition project to prioritize medical terminology, improving accuracy through targeted corpora, lookup-table correction, and fine-tuning with a modified loss function.”

“Built and deployed a production LLM-powered phone assistant for a healthcare clinic, combining streaming STT/TTS with RAG over approved clinic documents and strict safety guardrails to prevent unverified medical advice, plus seamless human handoff. Also has hands-on Apache Airflow experience building robust daily ML/data pipelines with data validation, retries/timeouts, monitoring, and metric-gated model deployment, and iterates closely with clinic staff using real call reviews.”

“Built and deployed a production RAG system for financial/compliance teams using GPT-4, Claude, and local models to retrieve and summarize thousands of internal documents with strong security controls (role-based retrieval, PII masking). Drove significant operational gains (30+ hours/week saved, ~35% productivity lift, ~45% faster responses) and orchestrated end-to-end ingestion/embedding/index refresh pipelines with Airflow, S3, and SageMaker while partnering closely with compliance stakeholders on auditability and traceability.”

“Built a production, real-time insurance claims document-understanding and fraud-detection pipeline using TensorFlow + fine-tuned BERT, deployed on AWS (SageMaker/Lambda/API Gateway) with automated retraining via MLflow and Jenkins. Addressed noisy documents and latency using augmentation and model distillation (3x faster), cutting claims ops manual review by ~50% and reducing fraudulent payouts.”

“Built and deployed an AI agent to help patients navigate complex housing information by scraping and normalizing unstructured data across all 50 U.S. states, then layering a LangChain RAG system with MMR re-ranking to reduce hallucinations. Experienced in orchestrating multi-agent workflows (LangGraph/CrewAI) and production reliability practices (Pydantic-validated outputs, LLM-as-judge evals, tracing). Also delivered stakeholder-facing explainability via SHAP dashboards for a loan-approval predictive model at Welspot.”

“Robotics/perception engineer focused on production-grade, real-time systems—optimized self-supervised segmentation on Jetson Nano from ~6–10 FPS to ~20–25 FPS and scaled experimentation/deployment by unifying 15+ edge models in a modular PyTorch Lightning framework. Experienced integrating distributed LiDAR-camera fusion via gRPC/protobuf into mission planning, migrating ROS1→ROS2 Foxy for multi-drone perception, and adding Prometheus-based observability for long-running deployments.”

“Software engineer with connected-car/automotive production experience who owned an end-to-end remote door lock/unlock feature and introduced unit testing (GTest) plus rig/simulator validation. Also built and productionized an AI-native AWS cloud cost assistant (Lex + GPT-based LLM + Lambda + RAG/vector DB) with guardrails and achieved 94% evaluation accuracy. Helped replace a third-party solution with an in-house build, saving the company ~€9M.”

“Built and shipped an AI-powered driver ranking/assignment system at AffirmoAI using LLM intent classification + RAG over pgvector/Postgres, served via FastAPI with a React UI that explains scores. Drove measurable improvements through optimization and iteration (latency down to <800ms, adoption 60%→90%+) and implemented rigorous eval loops with dispatcher ground truth plus cold-start handling for new drivers.”

“AI startup co-founder and CTO who helped raise $2.6M in seed funding at RedRex and drove product strategy from ideation through execution. Experienced with rapid experimentation and AI-driven prototyping to validate ideas quickly, and has direct exposure to the VC/accelerator ecosystem (Gener8tor) with a market-first approach to building and pitching startups.”

“Entrepreneur/fintech product builder who raised a $100K pre-seed from ex-Google/Microsoft execs and built a real-time, direct-to-vendor bill pay micropayments platform. Previously helped scale Norton LifeLock to 1M users (2003) and also created Karma LA, a fraud-resistant, verified donation system (including VA veteran verification) aimed at improving trust and conversion in giving.”

“Robotics software engineer with hands-on depth across simulation (Isaac Sim, Gazebo, Webots), ROS/ROS2 integration, and real-time embedded control. Led an end-to-end quadruped (12-motor) Isaac Sim build from Fusion 360 CAD-to-URDF through physics tuning to achieve a stable walking gait, and optimized a 5-servo arm by cutting IK compute time by 60%+ using lookup tables to eliminate jitter.”

“Recent UC Riverside master’s graduate focused on uncertainty-aware imitation learning for indoor robot navigation, building a full ROS 2 Humble stack (perception, learned policy, uncertainty estimation) with adaptive speed control. Demonstrated strong real-time robotics debugging and systems skills, achieving 92% autonomous navigation success across 100 trials and improving reliability through uncertainty calibration and SLAM/loop-closure optimization.”

“Built and shipped a production RAG question-answering system using LangChain/OpenAI, Docker, and FastAPI, then reduced hallucinations through disciplined retrieval tuning and constrained prompting. Also implemented a custom evaluation framework (QA-pair dataset) to measure faithfulness/relevance and deployed containerized ML microservices on AWS ECS/Fargate with ALB and rolling, zero-downtime updates.”

“Forward-deployed, full-stack/platform engineer who owns production features end-to-end across frontend, backend, data, and infrastructure (AWS serverless, Terraform, React). Has modernized critical fintech/payment systems (zero-downtime monolith-to-microservices with Kafka event sourcing) and productionized AI-native support workflows (LLM + RAG on Pinecone) with measurable gains in latency, incidents, CSAT, and support efficiency.”

“LLM/agent engineer who built a production multi-agent research automation system using LangGraph (planner, retriever with FAISS, supervisor, evaluator) with structured outputs and citation tracking for traceable reports. Emphasizes reliability and operations—LangSmith-based observability, multi-level testing, hallucination mitigation, and latency/cost controls—plus prior experience as a Computer Vision Software Engineer at Deepsight AI Labs working directly with non-technical customers.”

“Backend/platform engineer with hands-on ownership of Kubernetes GitOps delivery (GitHub Actions + Argo CD) on AWS EKS, including progressive rollouts and reliable rollback across interdependent microservices. Built a Python/FastAPI ML-driven document-processing service (PostgreSQL + S3) to complement existing Spring Boot systems, and implemented Kafka streaming pipelines with Schema Registry plus Prometheus/Grafana observability. Also supported a hybrid cloud-to-on-prem migration for compliance/latency with phased rollout and incremental PostgreSQL migration.”

“Graduate Assistant Intern at Friends University who built and deployed a GenAI-driven requirement understanding system that automates extraction and semantic grouping of technical requirements from large unstructured documents. Demonstrates strong LLM engineering rigor (golden datasets, regression testing, post-processing validation) and production-minded delivery using LangChain/LlamaIndex orchestration, FastAPI microservices, Docker, and cloud deployment.”