Vetted Computer Vision Professionals

“Built and deployed production LLM assistants for internal Q&A and customer-feedback summarization, emphasizing reliability (RAG, prompt tuning, validation/whitelisting) and privacy safeguards. Improved adoption by adding explainable outputs and a user feedback mechanism, and has hands-on orchestration experience with Aflow and Azure Logic Apps.”

“Built a production, real-time clinical documentation system at HCA that converts doctor–patient conversations into structured clinical summaries using speech-to-text, LLM summarization, and RAG. Demonstrated measurable gains from medical-domain fine-tuning (clinical concept recall +18%, ROUGE-L 0.62 to 0.74) while meeting HIPAA constraints via PHI anonymization and encryption, and deployed via Docker/FastAPI with CI/CD and monitoring.”

“Engineering/product leader who builds business-aligned technology roadmaps and scales pod-based orgs with strong delivery discipline (OKRs, CI/CD, QA automation). Led a SaaS supply-chain application adopted by Fortune 100 customers, citing ~$4M MRR and ~87% gross profit, and has hands-on experience standardizing LLM + cloud/MLOps architectures with security/compliance guardrails. Also created the PISEK methodology and used it to run distributed innovation sprints (e.g., an AI ETA predictor moved from pilot to production).”

“Robotics software engineer focused on perception data pipelines and multi-robot coordination. Built ROS 2 (rclpy) nodes for synchronized RGB/ToF/pose processing and scaled a perception training data generation pipeline from single-object to multi-object while preserving backward compatibility. Also has strong DevOps experience deploying containerized APIs on Kubernetes with Kustomize and automated releases via GitHub Actions.”

“Has production deployment experience delivering computer-vision systems on AWS (Docker + S3) including a GDPR-focused face/license-plate obfuscation pipeline and a semantic-segmentation project aimed at reducing annotation time. Worked closely with DevOps and frontend teams and partnered with CEO/CMO to present an AI-driven annotation workflow to non-technical VC stakeholders.”

“Research-focused AI/ML candidate who built an fMRI-based classifier to predict schizophrenia treatment effectiveness under small-dataset constraints. Demonstrated pragmatic model selection by moving from a complex GNN to graph-summary feature engineering with logistic regression, significantly improving accuracy and AUC; primarily works in Google Colab with script-based workflows.”

“Built and deployed an LLM-powered financial/regulatory document analysis platform at State Street, combining fine-tuned transformer models with a RAG pipeline over internal knowledge bases. Owned the productionization stack (FastAPI, Docker, SageMaker, Terraform, CI/CD) plus monitoring for drift/latency/hallucinations, delivering ~40% faster analyst review and improved reliability through chunking/embeddings and grounding.”

“Robotics software engineer focused on real-time state estimation and perception pipelines, with hands-on C++/ROS work improving LiDAR+IMU odometry stability via an iterative EKF and careful timing/synchronization fixes. Has integrated LIO-SAM, built multi-robot communication bridges (ROS + custom UDP with heartbeat/fallback), and uses Gazebo + Docker for repeatable testing, backed by CI/CD experience maintaining Azure DevOps pipelines at Cognizant.”

“IBM engineer who built and deployed a production RAG-based LLM assistant using LangChain/FAISS with a fine-tuned LLaMA model, served via FastAPI microservices on Kubernetes, achieving 99%+ uptime. Demonstrates strong practical expertise in reducing hallucinations (semantic chunking + metadata-driven retrieval) and managing latency, plus mature MLOps practices (Airflow/dbt pipelines, MLflow tracking, monitoring, A/B and shadow deployments) and effective collaboration with non-technical stakeholders.”

“Robotics software candidate focused on ROS 2 simulation work: integrated a SolidWorks-designed truck + dual-trailer CAD model into ROS 2 Humble by building URDF/meshes, fixing coordinate/joint axis issues, adding ros2_control + Gazebo integration, and implementing teleop. Also built a Python P-controller node for autonomous pose navigation and has experience with common robotics/embedded communication protocols and Docker-based ROS environment setup.”

“Robotics/AI candidate who built an AI-driven landmark location tool during a summer internship at Mobile Drive, combining YOLOv5 object detection with OpenStreetMap-based geolocation to handle dense, cluttered urban environments. Also researched deploying LLM-based agents on constrained hardware using quantization plus LoRA/continuous learning, improving accuracy from ~80% to ~92%, with an emphasis on production logging for reliability.”

“Built and deployed a production LLM-powered financial risk and compliance platform to reduce manual trade exception handling and speed up insights from regulatory documents. Implemented a LangChain multi-agent workflow with structured/unstructured data integration (Redshift + vector DB) and emphasized hallucination reduction for regulatory safety using Amazon Bedrock. Strong MLOps/orchestration background across Kubernetes, Airflow, Jenkins, and monitoring/testing with MLflow, Evidently AI, and PyTest.”

“At CVS Health, the candidate productionized a RAG-based LLM solution in a regulated healthcare setting, emphasizing reliable data pipelines, LoRA fine-tuning, monitoring, safety guardrails, and A/B testing. They have hands-on experience troubleshooting real-time RAG failures (e.g., chunking/embedding issues) and regularly lead developer-focused demos/workshops while translating technical architecture into business value for stakeholders.”

“Full-stack engineer at an early-stage startup building an agentic AI application for enterprise systems, combining customer-facing Next.js/React UI work (30% faster load times) with backend/workflow orchestration using FastAPI + n8n, Redis, and RabbitMQ. Previously at Deloitte USI, built BDD Selenium/Java automation and managed 200+ defects end-to-end using JIRA/JAMA to support on-time production releases.”

“Lead hardware/integration engineer on a high-speed autonomous drone for adversarial drone tracking and surveillance, owning the full stack from custom electronics and flight-controller tuning to ROS2 SLAM and GPU-accelerated computer vision. Built a custom USB-based ROS2 LiDAR driver and migrated SLAM from SLAM Toolbox to Google Cartographer, while boosting YOLOv5 tracking from ~2 FPS to ~45 FPS on an NVIDIA Jetson through CUDA/dependency optimization.”

“Senior design robotics engineer on a "Grocery Robot" project selected for the final round of the $10K SICK Challenge, owning ROS2 system design and behavior-tree-based task orchestration across multiple independently developed modules. Also implemented I2C/ESP32 collision avoidance, IK control for a robotic arm, and a Node.js ordering system, with additional research experience using RPLIDAR-based SLAM.”

“Paystand engineer/intern who built a multi-agent LLM orchestration system (with logging/feedback loops) that became part of the team workflow and reportedly cut development time ~70%. Partnered with sales/product on enterprise demos and implemented a dynamic RBAC system that helped drive adoption of an intern-built product to multiple enterprise clients, contributing to seven-figure ARR. Also founded and pitched a student-entrepreneur business management/payments project (HustleHub) and won a university startup competition.”

“Built and evolved a geospatial ETL + API platform that processes pixel-wise satellite imagery in PostgreSQL/PostGIS into low-latency farm-level time-series metrics for an interactive dashboard, using precomputed hotspot analysis to reduce latency by 75–80%. Experienced in FastAPI-style API contract design (OpenAPI), caching, server-side filtering/compression, and production-minded security patterns (RBAC, session-derived authorization, password hashing) with disciplined rollback/versioning practices.”

“Robotics-focused engineer with hands-on projects ranging from a solo Dobot Magician Lite tic-tac-toe system (computer vision + minimax) to integrating an LLM with a Dobot arm for real-time pick-and-place via structured action outputs and validation. Also brings prior full-time DevOps experience (Docker/Kubernetes and CI/CD) and has used ROS/Gazebo for simulation work, including exploring improvements to crowd-aware navigation using human-trajectory datasets.”

“LLM/ML engineer who has shipped production systems in legal/financial-risk domains at Wolters Kluwer, including a hybrid OCR+deterministic+LLM extraction pipeline that structured UCC filings at massive scale and drove $6M+ in revenue. Also built LangGraph-based multi-agent “Deep Research” workflows with model routing, tool calls (MCP), persistence, and human-in-the-loop review, and partnered closely with policy writers to deliver LLM summarization that cut writing time by ~60%.”

“Software/IoT-focused candidate with startup internship experience building a planner AI service integrating Google Calendar (OAuth/token handling) and connecting AI, backend, frontend, and database. Also has embedded systems + AWS networking troubleshooting experience and has implemented TCP networking projects optimized for throughput and low jitter/latency; collaborates with clients via weekly meetings using Trello/Slack.”

“Built and deployed AcademiQ Ai, a production LLM-based teaching assistant using GPT/BERT with RAG (LangChain + Pinecone) to handle large student notes and generate adaptive explanations/quizzes. Demonstrated measurable retrieval-quality gains (18% precision improvement, 22% less irrelevant context) by tuning similarity thresholds and chunking based on user satisfaction signals. Also orchestrated terabyte-scale, real-time demand forecasting pipelines using Airflow and Kubeflow on GCP with strong monitoring, shadow deployment, and feedback-loop practices.”

“ML/AI engineer with healthcare payer experience (Signal Healthcare, Cigna) who has shipped production fraud/claims prediction systems using Python/TensorFlow and exposed them via FastAPI/Flask microservices integrated with EHR and Salesforce. Emphasizes operational reliability and trust—Airflow-orchestrated pipelines with data quality gates plus SHAP-based interpretability, A/B testing, and drift/debug workflows—backed by reported outcomes of 22% lower false payouts and 17% higher model accuracy.”