Vetted Computer Vision Professionals

“ML/LLM engineer who has shipped production RAG systems (LangChain + HF Transformers + FAISS) with hybrid retrieval and cross-encoder re-ranking, deployed via FastAPI/Docker/Kubernetes and monitored with MLflow. Also partnered with wealth advisors at Edward Jones to deliver a client retention model with SHAP-driven explanations and a dashboard that improved trust, adoption, and reduced high-value client churn.”

“GenAI/ML engineer who has shipped production agentic systems in highly regulated and high-throughput environments, including an AWS Bedrock-based fraud/compliance workflow at U.S. Bank with PII redaction and hallucination detection that cut investigation time by 50%+. Also built and evaluated RAG and recommendation systems at Target, using RAGAS-driven testing, hybrid retrieval with re-ranking, and SHAP explainability dashboards to align model behavior with merchandising business KPIs.”

“Full-stack engineer who built and owned a generative-AI pipeline end-to-end inside the Vibecut video editor using Next.js App Router/TypeScript, Gemini-based prompt routing, and Zustand state management, including concurrency-safe requests. Also integrated Python services to access newly released AI tooling, optimized Postgres/S3 data flows for thumbnails, and built Modal-to-Amplitude workflows for Reddit-driven sentiment/metrics in a pre-seed environment while also handling marketing.”

“AI/LLM engineer who built a production autonomous GenAI content ecosystem that generates short-form scripts, extracts viral highlights from long-form video, and dubs content into 33+ languages. Focused on making LLM outputs production-safe via schema enforcement, token-to-time alignment, critic-agent verification, and scalable async orchestration—cutting manual workflows by ~90% and saving $200k+ annually.”

“Unity developer with deep GPU compute experience who shipped a web-deployed CAD-style app requiring real-time mesh manipulation, solving performance and browser memory-limit issues via compute shaders and mesh chunking. Built an independent Unity gravity simulation using Schwarzschild approximation and geodesic integration, and has also worked on game-engine threading/job-queue architecture using AI-assisted workflows.”

“Built and productionized a multi-agent, LLM-powered document understanding system to replace manual review of long documents, using LangGraph orchestration plus RAG to reduce hallucinations. Implemented layered reliability controls (structured templates, checker agent, and human-in-the-loop feedback) and reported ~40% speed improvement after orchestration; also has hands-on Airflow experience for scheduled data pipelines.”

“Built and deployed a production long-form article summarization system using BART/T5/PEGASUS, tackling real-world constraints like token limits, latency/quality tradeoffs, and factual drift via chunking/merge logic and constrained decoding. Uses pragmatic Python-based pipeline orchestration (scheduled jobs, modular scripts, logging/retries) and iterates with stakeholder feedback to make outputs genuinely useful for content workflows.”

“LLM/ML platform engineer with hands-on experience taking an LLM document summarization prototype into a production-grade service on AWS EKS, emphasizing low-latency inference, drift monitoring, and safe CI/CD rollouts (canary + rollback). Strong in real-time debugging of agentic/RAG systems (tracing, retrieval/index drift fixes) and in developer enablement through practical workshops (Docker/Kubernetes/FastAPI) plus pre-sales support via demos and benchmarks to close pilots.”

“Founding AI engineer (June 2024) at Talon Labs who built and productionized an LLM-powered chatbot for interacting with proprietary supply-chain documents, deployed at large scale (25–100,000 users). Experienced with RAG/LLM orchestration (LangChain, LlamaIndex, Groq AI) and production ops tooling (Kubernetes, Docker, Kubeflow, Airflow), with a metrics-driven approach to evaluation, observability, and stakeholder alignment.”

“Senior technology leader and hands-on builder spanning enterprise architecture and product/engineering leadership across healthcare and entertainment. Has led high-impact cloud and security architecture decisions (including establishing a private cloud to address scalability/security at massive scale) and scaled orgs 300% using pod-based team structures. Currently building an AI-supported hydroponics/vertical farming IoT framework (ESP32 + Azure) and a musician collaboration platform (React + Neo4j + AWS).”

“Backend-focused Python/Flask engineer who owned REST APIs for a video analysis system, including preprocessing, ML inference integration, and post-processing into time-aligned predictions consumable by a React UI. Demonstrated practical performance/scalability work by decoupling API request handling from CPU-heavy processing and adding timing instrumentation to identify and optimize bottlenecks.”

“Robotics software engineer with deep ROS/ROS 2 autonomy experience across warehouse fleets (Knapp delivery robots and quadrupeds), spanning SLAM, EKF-based sensor fusion localization, Nav2, and behavior-tree mission orchestration. Built a simulation-first testing approach using Isaac Sim Replicator with Dockerized, statistically analyzed repeat runs to catch nondeterminism, and personally owned real-world validation. Also developed a custom UR10 singularity-check ROS node based on manipulability.”

“Finance-domain ML/LLM engineer who has shipped production systems including a RAG-based financial insights assistant with a custom post-generation validation layer that verifies atomic claims against retrieved source text to prevent hallucinations in compliance-critical workflows. Also built large-scale MLOps automation on AWS using Kubeflow + MLflow + CI/CD for fraud detection and credit risk models processing 500M+ transactions/day with a 99.99% uptime goal, and partnered closely with JP Morgan risk/compliance stakeholders on NLP-driven compliance monitoring.”

“Backend engineer who built an AI voice-driven calendar assistant using Python/Flask with a multi-agent architecture (CrewAI + OpenAI), including agent-specific memory management and summarization to handle token limits. Also has production performance optimization experience at Trendyol, adding Redis caching to speed up an advertisement retrieval endpoint and improve page load experience.”

“LLM/GenAI engineer with US Bank experience building a production financial-document intelligence platform using LangChain/LangGraph, GPT-4, and Amazon OpenSearch. Delivered a RAG-based assistant for compliance/audit teams with grounded, cited answers, focusing on reducing hallucinations and latency, and deployed securely on AWS (SageMaker/EKS) with CI/CD and evaluation tooling (LangSmith, RAGAS).”

“Currently a startup CTO who thrives on building new technology stacks and rapidly turning technical ideas into products. Interested in partnering with a CEO/business team to commercialize embedded/edge concepts such as multi-sensor drone localization (video/audio/RF with SDR), low-cost solar+battery power nodes networked via LoRa, and an Amazon Sidewalk/LoRa connectivity device with cloud management.”

“Built and deployed a production LLM-powered clinical insights/summarization assistant for healthcare teams, including a Spark+Airflow pipeline, fine-tuned transformer models, and a FastAPI Docker service on AWS. Demonstrates strong MLOps/LLMOps depth (Airflow on Kubernetes, custom AWS operators/IAM, MLflow, CloudWatch) and practical reliability work like hallucination mitigation, confidence scoring, and retrieval-backed evaluation with shadow deployments.”

“ML/LLM engineer who has deployed a production LLM-powered assistant for intent classification and query routing (order recommendation/support deflection), combining BERT fine-tuning with an embedding-based retrieval layer and optimizing for low-latency inference. Experienced with end-to-end reliability practices—Airflow-orchestrated ETL, data validation/alerting, MLflow experiment tracking, and iterative improvements driven by user feedback and monitoring.”

“Currently working in CoRAL Lab, built and deployed IntegrityShield—a document-layer PDF watermarking system that keeps assessments visually identical while disrupting LLM-based solving; validated in a real classroom where it helped catch 12 AI-cheating cases. Also built MALDOC, a modular red-teaming platform for document-processing AI agents using LangGraph to run reproducible, deterministic adversarial trials across OCR/text/vision routes.”

“Built and deployed a production RAG-based internal knowledge assistant that let analysts query company documents in natural language, using LangChain/LangGraph with Pinecone and a FastAPI service for integration. Emphasizes reliability in production through hallucination mitigation (retrieval tuning + prompt guardrails) and measurable evaluation/monitoring (accuracy, latency, task completion, hallucination rate), iterating based on user feedback.”

“Entrepreneur building an applied-AI tool for geological resource exploration (critical minerals, oil & gas) that overlays proprietary and public data from reports/logs/maps to generate evidence-based greenfield profiling insights. Has spent ~2 years on industry research, built a POC, validated demand with purchasing signals, and developed partnerships/network including USGS, DARPA, and ESRI.”

“Robotics software candidate with experience spanning simulation (MuJoCo, Gazebo, Webots) and ROS1/ROS2 development, including hardware-oriented work on a hexapod and a Mecademic Meca500 R3 arm. Built a visually guided interactive indoor robot system using a CV pipeline plus POMDP + imitation learning with PPO-based residual RL, and has practical debugging experience improving LiDAR SLAM stability and migrating sensor interfaces from ROS1 to ROS2.”

“Robotics software engineer with hands-on ROS2 + Autoware perception experience, focused on building benchmarking infrastructure for object detection models inside a real-time autonomous driving stack. Strong in evaluation rigor (synchronization, deterministic playback, format standardization) and practical ROS2 debugging/validation workflows using RViz and Gazebo.”

“Backend engineer who has shipped production LLM-powered features, including an AI-assisted developer tool on AWS (Spring Boot) and a blog platform capability using embeddings + Elasticsearch for semantic retrieval and LLM-generated summaries/recommendations. Demonstrates practical tradeoff management (quality/latency/cost), guardrails to reduce hallucinations, and evaluation-driven iteration using real user queries and observability via ELK.”