Vetted MLOps Professionals

“Hands-on infrastructure and engineering leader (Director of Global Infrastructure / CTO) who has run double-digit multi-million dollar data center expansion and cloud migration programs and scaled teams rapidly (including offshore/nearshore). Strong AWS and microservices background (Lambda/SQS/SES), with experience balancing deep technical architecture work alongside investor/VC communications and fundraising-related responsibilities.”

“Robotics software engineer focused on backend/integration for indoor autonomous mobile robots, with hands-on ROS 2 experience integrating Nav2/AMCL/TF2 and LiDAR/camera pipelines. Emphasizes production readiness—robust failure recovery, QoS-tuned distributed communication, and strong observability (logging/health checks)—validated through Gazebo simulation, sensor-data replay debugging, and Docker-based CI/CD deployment.”

“LLM/agentic systems builder who has deployed production solutions for a resource management firm, using an MCP-driven architecture with Neo4j + Elasticsearch and a ChatGPT frontend to generate candidate/company “SmartPacks” and answer entity Q&A. Also built a LangGraph/LangSmith-orchestrated multi-agent workflow that automates data-infra change requests end-to-end (impact analysis, SQL + tests, and PR creation), and delivered a ~60% latency reduction through TTL-based context caching while improving accuracy via a business data dictionary.”

“Player-coach engineering leader who stayed hands-on (coding/reviews) while leading delivery, including designing an event-driven AI workflow engine with explicit state modeling and robust retries. Built near real-time enterprise analytics for campaign measurement and drove reliability/process improvements (observability, incident runbooks, release management). Introduced lightweight CI/CD and automated testing to cut release time by ~40% while maintaining quality.”

“AI/ML engineer who shipped a production blood-test report understanding and personalized supplement recommendation product, using a LangGraph multi-agent pipeline on AWS serverless with OCR via Bedrock and RAG over vetted clinical research. Also built end-to-end recommender system pipelines at ASANTe using Airflow (ingestion, embeddings/features, training, registry, batch scoring/monitoring) with KPI reporting to Tableau, with a strong focus on safety, evaluation, and measurable reliability.”

“CTO/Chief Architect who rebuilt an IoT platform from a fragile legacy stack into an AWS-based, multi-tenant cloud-native system supporting 50k+ connected devices and 10M+ monthly events, then layered in real-time data pipelines and ML anomaly detection. Known for tightly aligning roadmaps and OKRs to business KPIs (onboarding speed, uptime, velocity) and for scaling teams into domain-focused pods; previously led a shift from LAMP to event-driven Node.js microservices using MQTT and message queues.”

“Backend-focused Python engineer who owned high-traffic internal services end-to-end (FastAPI/Django) including REST/GraphQL APIs, PostgreSQL optimization, async task processing via SQS, and full CI/CD. Strong Kubernetes-on-EKS and GitOps (ArgoCD + Helm) experience, plus Kafka real-time streaming work and phased cloud-to-on-prem migration support.”

“PhD researcher (University of Utah) who built a production RAG-powered Virtual Reality Research Assistant to answer lab research questions with concrete citations. Implemented an end-to-end LangChain pipeline using PyPDFLoader, chunking strategies, OpenAI embeddings, and ChromaDB, with emphasis on grounding to reduce hallucinations and ensure research-grade accuracy. Collaborated closely with a non-technical PhD advisor to scope requirements, manage cost constraints, and demo iterative progress.”