Vetted Computer Vision Professionals

“ML/NLP practitioner focused on financial-services document intelligence and compliance workflows—built an end-to-end pipeline to classify documents and extract financial entities from loan applications, emails, and statements stored in S3/internal databases. Strong in entity resolution/record linkage and in productionizing pipelines with GitHub Actions CI/CD, testing, data validation, and Docker, plus semantic search using OpenAI embeddings and a vector database.”

“Infrastructure/operations engineer with hands-on production IBM Power/AIX (AIX 7.x, VIOS, HMC) and PowerHA/HACMP clustering experience, including DLPAR changes, failover testing, and incident recovery. Also delivers modern cloud DevOps work—GitHub Actions CI/CD for Docker-to-Kubernetes on AWS and Terraform-based provisioning of core AWS infrastructure (VPC/EKS/RDS/IAM) with controlled rollouts and drift checks.”

“Full-stack product engineer who has shipped polished customer-facing experiences across iOS (SwiftUI), web (Next.js/React/TypeScript), and Python backends. Built systems ranging from an escalating smart-reminder engine to a sub-200ms search UI over 6M+ court records, and owned AWS production operations including resolving a real DB-connection-exhaustion incident with scaling and architectural hardening.”

“Built and productionized an LLM-powered support/knowledge pipeline using embeddings and retrieval (RAG) to deliver more grounded, higher-quality responses while reducing manual effort. Focused on real-world reliability and performance—adding structured validation/guardrails, optimizing vector search and context size for latency/scale, and monitoring failure patterns in production. Experienced with orchestration via LangChain for LLM workflows and Airflow for production data/ML pipelines, and iterates closely with operations stakeholders through demos and feedback.”

“AI/LLM engineer who has shipped production RAG systems (including a 250K-document compliance knowledge tool on AWS) and focuses on reliability via citations, guardrails, and rigorous evaluation (Ragas/Opik/DeepEval). Also built a LangGraph-orchestrated webcrawler agent that cut research paper extraction from hours to minutes, and collaborated with clinical teams to deliver patient volume forecasting with an optimization layer for staffing.”

“Backend engineer who built an AI-driven "Smart Feedback Analyzer" API (Flask → FastAPI) that processes user feedback with NLP (Hugging Face + OpenAI) and returns structured insights. Demonstrates strong production-minded architecture: stateless services, Cloud Run + Docker deployment, Redis/Celery background processing, and Postgres/SQLAlchemy performance tuning (EXPLAIN ANALYZE, indexing, N+1 fixes), plus multi-tenant data isolation via JWT/API-key derived tenant IDs.”

Fatemeh Taghvaei

Screened

Junior AI/ML Engineer and Instructor specializing in deep learning, computer vision, and NLP

Chicago, IL2y exp

“Computer-vision practitioner and educator who built a real-time license plate recognition system (OpenCV/Python + KNN) optimized to run on a Raspberry Pi with camera integration. Also designs hands-on deep learning coursework, incorporating recent transformer-based vision research (Vision Transformers) into practical labs on real datasets.”

Nisarg Shah

Screened

Junior Machine Learning Engineer specializing in geospatial analytics and computer vision

Tempe, Arizona1y exp

“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.”

Pree Simphliphan

Screened

Intern Software Engineer specializing in robotics, embedded systems, and AI

Thailand1y exp

“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.”

Savya Patel

Screened

Junior Mechatronics Engineer specializing in robotics, embedded systems, and mechanical R&D

Watertown, MA3y exp

“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.”

Kyle Vo

Screened

Junior Software Engineer specializing in web, mobile, and embedded systems

Davis, CA1y exp

“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 shipped production LLM agents including an AI patient appointment assistant for Kyron Medical that automated specialist matching and end-to-end booking with email/SMS confirmations and a voice mode. Strong focus on production reliability (double-booking prevention with DB constraints and pre-write checks), deterministic multi-step orchestration with LangGraph, and rigorous monitoring/evaluation using LangSmith trace replay for prompt regression testing.”

“Analytics professional with Intuit experience spanning modern data stack work, behavioral segmentation, and applied AI. They built dbt/Snowflake pipelines powering retention and churn dashboards, automated feedback classification with OpenAI/LangChain, and partnered closely with product and marketing teams to turn analytics into onboarding, targeting, and lifecycle messaging decisions.”

“Built and owned VividCraft end-to-end, a production AI platform spanning a TypeScript/Next.js frontend and a Python backend with FastAPI, Celery, Redis, PostgreSQL, and AWS. Stands out for reliability-focused systems thinking: designed idempotent job orchestration across 9 AI providers, shipped with extensive automated test coverage, and reports zero production regressions after launch plus zero credit loss through provider outages.”

Ellen Lo

Screened

Mid-level Game Developer specializing in XR, AR, and multiplayer Unity experiences

Remote5y exp

“Gameplay programmer with hands-on experience shipping VR systems in Unity/C#, including a quest-based onboarding flow for the VR MOBA Crystal Conquest that became a core part of the player experience as the game expanded. Brings a mix of gameplay architecture, Photon Fusion multiplayer implementation, Quest optimization, and practical AI-assisted development workflows using Claude Code and Unity MCP.”

“Gameplay engineer with hands-on ownership of a computer-vision body-tracking system that powered Dance Break, a full-body dance game shipped across Windows, Mac, iOS, and Android and used at large live events like EDC Las Vegas and Alibaba CoCreate. Particularly strong in performance-sensitive Unity systems, cross-platform optimization, and multiplayer architecture decisions, with experience spanning mobile, VR, and deterministic networking.”

“Built from scratch a social media analytics MVP featuring an LLM-powered semantic search agent that became a core part of the product experience within a 6-week deadline. Stands out for focusing on production readiness early—retrieval-first design, explicit tool constraints, structured outputs, idempotent services, and practical eval/monitoring loops rather than demo-only AI.”

Lokesh Jain

Screened

Senior AI/ML Engineer specializing in supply chain and healthcare systems

Bentonville, AR6y exp

“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.”

“Graduate student who has built both full-stack web products and an offline Android AI assistant for visually impaired users. Particularly interesting for roles spanning product engineering and applied AI, with hands-on experience in on-device ML, privacy-sensitive deployment, and making complex AI interactions usable for non-technical users.”

“General Manager of Animoto and longtime product leader who has led zero-to-one builds and major rebuilds across video, insurance, automotive, and web products. Most notably, they transformed Animoto’s product into a more flexible creation platform, driving 80% higher conversion, doubling LTV, tripling NPS, and cutting video creation time from roughly 100 hours to 20. They also bring practical AI product experience and a clear human-centered philosophy shaped by tutoring and family ties to education.”

“Built and shipped enterprise AI products including a conversational SQL analytics platform and a production RAG system at Johnson & Johnson. Combines full-stack engineering with LLM systems expertise, and has delivered measurable impact at scale, including 48% lower retrieval latency and 37% better response relevance across 12M+ records.”

Ahmed Khalafalla

Screened

Entry-level Software Engineer specializing in full-stack and backend development

Boston, MA1y exp

“Frontend/full-stack web developer with hands-on experience building browser-based applications including a social-style platform and a banking web app. Stands out for practical performance optimization work—separating frontend/backend architecture, designing APIs, reducing unnecessary rendering, and improving UI clarity through iterative user testing.”

Kuwarpreet Singh

Screened

Junior ML and Full-Stack Engineer specializing in AI/ML applications

Buffalo, NY3y exp

“Master's-trained candidate with an AI/ML specialization who combines hands-on machine learning project work with practical AI-assisted software development. They actively use Claude, ChatGPT, and Claude Code for debugging, API integration, multi-file refactoring, and end-to-end feature scaffolding, while emphasizing validation against official docs before shipping.”