Vetted Computer Vision Professionals

“ML/NLP practitioner who has delivered production systems in regulated domains, including a healthcare compliance pipeline using RAG (GPT-4/Claude) plus TF-IDF retrieval that increased document review throughput 4.5x. Also has hands-on experience improving fraud detection data quality via entity resolution (Levenshtein, Dedupe.py) validated with A/B testing, and building scalable, monitored workflows with Airflow, CI/CD, and AWS SageMaker.”

“Unity gameplay programmer with Photon-based multiplayer experience who built a real-time player feedback system (instant animation/VFX/audio + score updates) using events/ScriptableObjects to keep gameplay and UI decoupled. Has tackled movement desynchronization under variable latency with client-side prediction/interpolation and uses AI tools to speed up prototyping and balancing for a procedurally generated, math-based casual game.”

“Robotics software engineer from Bigbot who led localization and perception for an outdoor autonomous delivery robot, building ROS2/Nav2-based autonomy with EKF sensor fusion (IMU/odometry/GPS) and perception-driven dynamic costmaps. Experienced taking systems from Gazebo simulation to real-robot deployment, optimizing real-time behavior via logging-driven debugging and latency reduction, and integrating heterogeneous comms (MAVROS/MAVLink, UART/CAN, MQTT) for distributed and multi-robot setups.”

“Robotics software engineer with hands-on hardware integration who built an AI-enabled smart dog door using a Raspberry Pi, camera-based recognition (DeepFace adapted for dogs), and stepper motor control (TB6600/NEMA 17). Experienced in ROS/ROS 2 across perception-to-controls, rigorous bag-driven debugging of SLAM/navigation issues, and deploying robot software with simulation-in-the-loop testing plus Docker/Kubernetes CI/CD.”

“Built and deployed a production LLM-powered text-to-SQL/document intelligence chatbot on AWS that lets non-technical business users query complex enterprise databases in plain English. Demonstrates deep practical expertise in schema-aware prompting, embeddings-based schema retrieval, SQL safety/validation guardrails, and rigorous offline/online evaluation with human-in-the-loop approvals for risky queries.”

“Built and deployed a production LLM-assisted recommendation and insights platform that unifies structured, semi-structured, and unstructured data via a modular ingestion pipeline, canonical schemas, embeddings, and late-fusion modeling. Experienced in operationalizing ML/LLM systems with Airflow and Kubernetes (Dockerized services, autoscaling, rolling updates) and emphasizes reliability through layered testing, guardrails, monitoring, and A/B experimentation while partnering closely with non-technical stakeholders.”

“Built a production-grade, semi-automated document recognition and classification system for large volumes of scanned PDFs, starting from little/no labeled data and handling highly variable scan quality. Deployed on AWS using SageMaker + Docker and orchestrated on EKS with a microservices design that scales CPU-heavy OCR separately from GPU inference, with strong reliability controls (validation, fallbacks, retries, readiness probes).”

“Backend/full-stack engineer with experience building customer-facing contact-center automation (agent assignment) and internal editorial/data operations APIs for life-sciences ontology management. Strong in microservices and event-driven systems (Spring Boot + Kafka), third-party integrations (Genesys/Five9), and pragmatic iteration via MVP scoping, tight stakeholder demos, and observability-focused reliability.”

“Software engineer who deployed and stabilized a real-time analytics platform at Senecio Software, focusing on production reliability, observability, and performance under load. Experienced debugging issues spanning distributed services and networking (e.g., tracing timeouts to packet loss from misconfiguration) and extending Python (FastAPI/Django) APIs for customer-specific analytics features in a configurable, maintainable way.”

“Machine learning robotics engineer with internship experience deploying object detection and semantic segmentation models to an autonomous vehicle fleet operating in airports and naval docking stations, optimizing with ONNX/TensorRT for NVIDIA Jetson edge deployment. Also built ROS/ROS2-based decentralized multi-drone coordination (TF trees, shared telemetry) validated in Gazebo and networked via Nimbro with sub-10ms latency messaging.”

“Built AI-EDU, an AI/LLM-powered learning platform created for a Technology Entrepreneurship class that predicts student engagement and generates personalized learning insights. Emphasizes strong data preprocessing/feature engineering on noisy student data, and has experience operationalizing workflows with basic Airflow/Prefect plus reliability practices (edge-case testing, metrics, logging, guardrails) and stakeholder-friendly dashboards/summaries.”

“Graduate student taking advanced coursework in NLP, generative image modeling, and computer vision; built a PPO reinforcement-learning agent for a Super Mario platformer with careful reward shaping and metric-driven evaluation. In a recent internship designing credit risk models, created a 10-method feature-selection voting framework and achieved ~10% out-of-sample performance improvement while reducing features to mitigate overfitting.”

“Game/VR simulation developer who built and shipped multiple VR training levels (e.g., nursing and scientific method) at VXR Labs, owning level implementation and backend logic. Experienced in Unreal Engine 5 Blueprints prototyping (including a horde mode) and in designing tightly gated, step-based educational experiences while collaborating closely with educators/subject-matter experts to balance realism with VR feasibility.”

“Data/ML practitioner who has built a production RAG-based knowledge assistant integrated into Microsoft 365/internal dashboards to help employees query internal documents in plain English. Experienced orchestrating and hardening ETL pipelines with Airflow and Azure Data Factory (validation, retries, monitoring) and running end-to-end model evaluation and production performance tracking via Power BI.”

“Built and deployed a production RAG-based clinical decision support assistant at MedLib, focused on fast, trustworthy answers from large medical documents. Demonstrates deep practical experience improving retrieval accuracy (semantic chunking + metadata-aware search), controlling hallucinations with grounded generation and thresholds, and adding clinician-requested citations using chunk metadata, with evaluation driven by healthcare professional review.”

“LLM engineer/data analyst who built a production RAG QA assistant over the Jurafsky & Martin NLP textbook to reduce hallucinations and provide explainable, source-grounded answers. Experienced with LangChain/LangGraph orchestration, retrieval optimization (embeddings, vector DBs, caching), and rigorous evaluation/monitoring (Retrieval@K, A/B tests, telemetry/drift). Previously communicated analytics insights to non-technical stakeholders at GS Analytics using Power BI and simplified reporting.”

“Built and shipped a real-time AI robotic inspection system, using a synthetic data generation pipeline to address rare edge cases—cutting data collection costs ~60% and boosting hard-scenario accuracy ~20%. Experienced in productionizing ML on constrained Jetson hardware and orchestrating end-to-end ML workflows with Airflow/Docker/Kubernetes, with a metrics-driven approach to reliability, evaluation, and stakeholder communication.”

“Data scientist who has built production LLM agents (GPT-4o + LangChain + RAG) to automate analyst-style ad hoc CSV analysis with guardrails and GPT-as-a-judge evaluation. Also delivered an explainable healthcare NLP system for ICD code classification by collaborating closely with clinicians, using a hybrid rule-based decision tree + BERT model to reach 97% accuracy and cut manual review time.”

“Startup-focused builder who created and iterated an MVP for Enky, a two-sided marketplace connecting music artists and creators, informed by hundreds of customer interviews. Implemented CI/CD, monitoring (PostHog/Sentry), and a complex payout pipeline involving scraping social platforms and routing escrow payments via Stripe, and has a track record of quickly debugging production issues (e.g., iOS-specific OAuth cookie failures).”

“Built and shipped a production AI/LLM agent-based, event-driven credit underwriting/decisioning workflow that automated document understanding, retrieval, risk scoring, and compliance checks—cutting turnaround from ~90 days to ~5 minutes while boosting throughput 200x+ and approvals ~50%. Experienced with Airflow/Prefect orchestration, Redis/RabbitMQ queues, rigorous eval/monitoring, and close collaboration with non-technical underwriting teams.”

“Built and shipped a production multi-agent LLM system at Solena that automated internal project intake, validation, reporting, and stakeholder communications using Python, SQL, and LangChain, with strong emphasis on reliability (structured validation, safe defaults, logging, and state tracking). Also used LangGraph to orchestrate a multi-step video summarization pipeline, and has experience partnering with non-technical stakeholders to define “completion” criteria and reporting needs.”

“Data science professional who has built a production RAG-based LLM question-answering system ("Flash Query") to deliver fast, accurate answers over large document collections, focusing on retrieval quality and grounded responses. Also collaborates with non-technical retail/jewelry stakeholders to turn business questions into predictive models and dashboards for decision-making.”

“Ingeniero de Robótica y Automatización con 10 años de experiencia en instalaciones robóticas (principalmente sector aeronáutico) y proyectos de investigación en UK. En una gran empresa ferroviaria programó desde cero una instalación con 3 robots Staubli con tracks y desarrolló una plataforma de virtualización/simulación en ROS 2 (Gazebo/MoveIt 2), incluyendo integración industrial OPC UA/Profinet con PLC para 21 ejes y visualización en tiempo real.”