Vetted Computer Vision Professionals

“AI-focused engineer with Infosys experience building Azure/.NET chatbot applications and recent hands-on work with FastAPI/LangChain. Built a hackathon multi-agent legal counsel system showcasing agent orchestration, and emphasizes production readiness via Docker, GitHub Actions CI/CD, pytest automation, and adversarial simulations for auditable AI behavior. No direct robotics/ROS experience to date.”

“LLM/agent engineer who has built production multi-agent systems (LangChain/LangGraph) for enterprise workflows like email and calendar automation, with a strong focus on latency, tool-calling accuracy, and evaluation via LangSmith. Also worked on AI long-term memory using knowledge graphs at VEAI and communicated the approach and tradeoffs to CEO/CTO stakeholders.”

“Built a production RAG chatbot for Worcester Polytechnic Institute that indexes 500+ webpages using FAISS + Llama 3, with strong grounding/hallucination controls (confidence thresholds and citations). Also has internship experience orchestrating multi-step ETL pipelines with AWS Step Functions and delivered a 30x faster fraud/claims triage workflow at Munich Re using association rules and stakeholder-friendly dashboards.”

“Candidate has hands-on experience at Cognizant building production-grade automation and integration solutions across Python ML services, Java microservices, Kafka, and Selenium-based UI testing. They stand out for a strong reliability mindset—covering failure modes, observability, flaky test hardening, and translating ambiguous payment-system business processes into resilient end-to-end automated workflows.”

“Healthcare-focused ML/AI engineer who has built production voice agents and clinical question-answering systems end-to-end, from experimentation through deployment, observability, and iteration. Particularly strong in making LLM systems reliable in real workflows via RAG, fine-tuning, guardrails, evaluation pipelines, and shared Python tooling; cites ~20% clinical QA accuracy gains and ~40% faster physician decision turnaround.”

“Full-stack engineer with payments/settlement domain experience who modernized a payment tracking workflow from REST to GraphQL and delivered a production payment status dashboard using Next.js App Router + TypeScript. Strong in performance and reliability work (Postgres indexing/Explain Analyze, Redis caching, Datadog observability) and in durable event-driven processing with Kafka (DLQs, idempotency, reconciliation, event replay).”

“AI/ML engineer who led Impacter AI’s production deployment of a specialized outreach LLM (CharmedLLM) fine-tuned on GPT-4.1, cutting API costs ~40% while boosting outreach effectiveness ~60%. Built the supporting MLOps and data infrastructure (MLflow, Kubernetes, PySpark, Kafka) and has agentic AI experience from University of Dayton, using LangChain + RAG and vector search (Pinecone) to improve reliability and reduce hallucinations.”

“Application-focused software engineer working on AI-heavy products, with hands-on experience building end-to-end document processing, retrieval, and configurable workflow systems. Particularly strong in combining React/TypeScript UX, FastAPI/Postgres backend design, and LLM workflow reliability improvements through validation, prompt iteration, and reusable abstractions.”

“Backend engineer who has both shipped production systems at work and independently launched tab-review.com, a BI dashboard quality/linting platform. They combine pragmatic early-stage execution with strong instincts around scaling, observability, RBAC, and production reliability, and have also built internal AI-assisted reporting workflows using LangChain and human-in-the-loop review.”

“Longtime Aptima product leader who turned a nominal DoD measurement 'suite' into a configurable cross-branch performance assessment platform deployed to Army, Navy, and Air Force users. Brings a rare mix of defense training, human performance science, AI/cybersecurity product work, and deeply user-centered UX thinking, including innovations like video tagging for live evaluation workflows.”

“Current Head of Transformation at Data Solutions Inc. leading a broad team spanning BI, analytics, data science, automation, project engineering, and software engineering. Has experience combining AI and computer vision with operational systems, including trading-post digitization, security-driven deployment controls, and executive-facing roadmap tradeoff decisions.”

“Built and deployed a production NLP sentiment analysis system at Piper Sandler to turn noisy, finance-specific customer feedback into scalable insights. Demonstrates strong end-to-end MLOps: fine-tuning BERT, improving label quality, monitoring for language drift, and automating retraining/deployment with Airflow and Docker (plus Kubeflow exposure).”

“ML/NLP engineer with Industrial IoT experience who built an end-to-end anomaly detection and GenAI explanation system: AWS (S3, PySpark, EC2/Lambda) pipelines feeding dashboards, plus transformer-embedding vector search to connect anomalies to noisy maintenance notes and past events. Demonstrated measurable impact (15% lift in defect detection; ~35% reduction in manual review; 35% fewer preprocessing errors) and strong productionization practices (orchestration, monitoring, rollback, data-quality controls).”

“Robotics software engineer with hands-on experience building an AGV for warehouse autonomy at Kick Robotics, working across SLAM, waypoint navigation, computer vision, and ROS2/RViz simulation. Demonstrated strong on-site troubleshooting by diagnosing a real-world mapping stall via log analysis (coordinate reset bug) and deploying a fix. Also has industrial automation experience coordinating SCARA robots via EtherNet/IP and multi-robot/swarm simulations using MQTT pub-sub.”