Vetted Computer Vision Professionals

“AI/ML engineer who has shipped production systems across computer vision and conversational agents: built a YOLOv8-based wheel fitment pipeline at a Techstars-backed automotive startup, focusing on sub-second latency, monitoring, and robust fallback mechanisms that drove 2–3x page view growth and +5–6k users. Also built a voice-based interview platform orchestrating Deepgram + GPT-4 Mini + OpenAI TTS with FSM-driven reliability, and has hands-on RAG experience (LangChain, hybrid retrieval, cross-encoder reranking, custom pseudo-query generation).”

“Software engineer/intern who built an EV charging station management platform from scratch (TypeScript/Next.js/Node/Express/Postgres) with real-time OCPP WebSocket operations and payment processing, iterating quickly based on operator feedback. Also created an internal CloudWatch log aggregation dashboard with Slack alerts that was adopted team-wide, addressing API rate limits and log-format inconsistencies through caching, pagination, and standardized parsing.”

“Built and deployed production RAG-based document search/Q&A systems (DocChat and an internship marketing RAG), using a React + FastAPI stack on GCP with docs stored in GCP buckets and retrieval via embeddings/vector DB. Emphasizes cost/performance tradeoffs (reported ~40% cost reduction) and ships via Docker (Railway), with load/API testing using JMeter and Swagger; regularly collaborates with a CEO stakeholder to iterate and push changes to production.”

“Built and operated large-scale biodiversity/ecological research platforms, integrating 50+ heterogeneous global datasets into a unified BIEN 3 schema on PostgreSQL/PostGIS and improving data consistency by 35%. Strong production engineering background (Linux monitoring, CI/CD performance gates, Docker on AWS/Azure) plus applied AI work building a Python RAG system (0.90 precision) and halving latency with Elasticsearch.”

“Robotics software engineer with hands-on SLAM, ROS2, and distributed multi-robot systems experience. Improved MAST3R-SLAM loop-closure place recognition by changing the ASMK/ASMKS retrieval similarity metric (L2→L1) and validated on 9 TUM sequences, keeping near real-time performance despite a 25–30% retrieval cost increase. Also tuned MoveIt motion planning for a 6-DOF arm (12% higher maze completion rate) and built MQTT mesh communications for ESP32-based AMRs, using Gazebo+Docker and CI-style automation for reproducible testing and deployment.”

“Backend engineer who owned the API layer for an AI trust/analytics dashboard (trust scores, stability checks, public verification endpoints) using Python/FastAPI and Postgres. Has hands-on DevOps experience deploying FastAPI and Node.js services to AWS Kubernetes with GitHub Actions + ArgoCD GitOps, plus Kafka-based real-time event streaming and careful staged migration practices (shadow traffic/dual writes, rollback planning).”

“Backend/cloud engineer from Resilient Tech with hands-on experience deploying REST APIs and database migrations into a live ERP used by real customers while maintaining 99% uptime. Has debugged intermittent AWS container timeouts down to security group/load balancer misconfigurations, and has extended Python in an ERPNext system to meet GST/e-invoicing compliance requirements with strong customer collaboration.”

“Robotics/SLAM-focused engineer who worked on RT-Appearance mapping using NetVLAD, replacing traditional CV feature extraction with a deep learning approach to improve loop closure in repetitive green environments. Has hands-on ROS1/ROS2 experience (including bridging), point-cloud alignment with G-ICP for sensor-parameter matching, and Gazebo+Docker simulation testing for motion planning/perception.”

“Indonesia-based software tester with hands-on experience testing payment software on EDC machines and manual web application QA, plus early exposure to Selenium automation. Pragmatic about deadline-driven triage—prioritizes critical issues and plans lower-impact fixes for future patches—while expressing strong interest in learning game-industry standards.”

“Built and deployed a production generative-AI copilot at Tungsten that automates invoice/form extraction template creation, reducing weeks of manual model-building work. Combines fine-tuned LLMs (PyTorch/HuggingFace) with OpenCV layout grounding to reduce hallucinations, and runs an end-to-end Kubeflow-based MLOps pipeline with drift monitoring, canary releases, and automated retraining.”

“Gameplay/physics-focused C++/UE5 developer who builds end-to-end real-time 3D systems (custom melee hit reactions, animation, and terrain movement) and debugs deep rendering issues using GPU/pixel-shader tooling. Has hands-on experience with complex networked physics, including ragdoll replication with bandwidth compression and hybrid deterministic/non-deterministic prediction approaches, and is interested in realistic soccer/football movement systems.”

“Robotics software engineer with ~3.5 years in ROS/ROS2 mobile robotics, SLAM, and control who owned end-to-end integration for a sim-to-real mobile platform (Zephyr), including ros2_control, EKF sensor fusion (IMU + Vicon), and Gazebo validation with quantified accuracy. Also built a multi-drone CSLAM stack integrating ORB-SLAM3 and PX4 offboard control, scaling via namespaces, synchronization/QoS discipline, and performance debugging with ros2_tracing.”

“Senior backend/data engineer who has built and maintained HIPAA-compliant, real-time clinical FastAPI services on AWS, orchestrating ML/LLM and vector DB calls with strong reliability patterns (auth, timeouts/retries, graceful degradation, idempotency). Also delivered AWS IaC/CI-CD (Terraform/Helm/GitHub Actions) across EKS/Lambda/SageMaker and built Glue/Spark ETL with schema evolution and data quality controls, plus demonstrated large SQL performance wins (15 min to <9 sec) and hands-on incident ownership.”

“New grad who shipped an LLM-powered writing app (“Write-it”) to production on Azure with CI/CD (GitHub Actions + JFrog) and implemented an unconventional RAG pipeline to prevent repetitive prompts using embeddings and cosine similarity. Also participated in a Luma AI image/video generation hackathon, iterating with artist feedback and improving usability by rewriting non-technical prompts via an LLM.”

“Backend/platform engineer who built and evolved a large-scale healthcare document processing system (OCR + LLM orchestration) in Python/FastAPI on Google Cloud (Cloud Run, GCS, Firestore), processing ~1.5M files per batch and tens of millions overall. Emphasizes reliability and operational safety via deterministic IDs, idempotent state machines, strong observability, and self-healing reconciliation, plus disciplined migrations using dual-run validation and incremental rollouts.”

“Undergraduate AI/robotics research assistant who built a city-scale robotics simulation stack for embodied question answering, including JSON-driven environment generation and a ROS 2 pipeline bridging Isaac Sim sensors (CV/LiDAR) to external ML/RL algorithms. Also created and deployed an open-source library workflow tool adopted across multiple local libraries, using GitHub Actions CI/CD for signed releases and automated updates.”

“Unity developer with broad cross-platform experience (HTC Vive VR, iOS/Android, Nintendo Switch, WebGL) who built a near-solo multiplayer AR iOS party game using Photon/PUN and AR Foundation. Demonstrates strong practical networking and performance optimization skills, including lag compensation and client-side shader-based rendering strategies.”

“Software engineer with experience spanning healthcare middleware (patient records + insurance integration) and an AI fantasy football product built with React/TypeScript, Firebase, API gateways, and pandas-based data pipelines. Has hands-on microservices scaling experience (latency mitigation, async migration, state-based redesign) and built an internal feature-toggle dashboard that improved demo efficiency and sales outcomes.”

“Built and deployed a production AI accessibility platform that turns chart and image-based graphs into real-time audio narratives for visually impaired users. Implemented a ResNet-based CV + OCR + NLP + TTS pipeline and improved performance through preprocessing, Redis caching, and Kubernetes autoscaling/rolling updates on AWS to handle traffic spikes with no downtime.”

“AI Researcher with production experience building a real-time computer-vision detection pipeline augmented by an LLM-based verification layer to cut false positives (~78%) and reach ~90% real-world accuracy. Also partners cross-functionally with Product/Sales/Marketing to shape AI feature prioritization and market positioning using analysis and interactive dashboards.”

“Graduate-school lab engineer who built and owned the final architecture of a Microservices Hub that integrates REST APIs, issues API keys, monitors 10+ Linux servers, and visualizes service dependencies via a topology graph. Strong in bridging legacy and modern stacks (Dockerized and non-Dockerized services like Apache/screen) using deep Linux/networking knowledge, plus practical real-time audio streaming for STT/TTS and experience mentoring others.”

“Backend engineer with experience modernizing legacy systems and building modular Python/Flask services, including a REST-to-GraphQL migration for an e-commerce platform that improved API response time by 45%. Strong in performance and scalability work across PostgreSQL/SQLAlchemy (indexing, JSONB, N+1 fixes, connection pooling) and high-throughput systems (Celery + Redis), plus integrating ML microservices with TorchServe, Kafka streaming, feature stores, and Prometheus/Grafana monitoring.”

“Backend Python engineer who owned the full backend build of an AI-driven platform for UK golf clubs, including FastAPI microservices, vector search, and a tuned LangChain+Pinecone RAG pipeline focused on cost and hallucination reduction. Experienced deploying Django/FastAPI/Flask stacks on AWS-backed Kubernetes with GitOps/ArgoCD-style delivery, plus executing legacy-to-AWS migrations and building Kafka-based real-time analytics pipelines.”