Independent staff-level engineer focused on event-driven ML backends, inference pipelines, and the supporting infrastructure required to operate AI systems in production. M.S. Physics, 15+ years across full-stack, embedded, RF, and safety-critical software. Two issued U.S. patents.
Focused on AI infrastructure: the systems that move data into models, models into production, and predictions back into your business. A few representative outcomes from prior work across commercial AI, defense R&D, and safety-critical systems.
Architect and ship Kafka-based inference pipelines that route streaming events to the right model at the right time; fanning combined event streams out to multiple models with attention to throughput, ordering, and failure modes.
Production microservices in FastAPI and Django exposing REST and gRPC interfaces, backed by Postgres, MySQL, and Redis, and integrated with Kafka.
Docker, Kubernetes, Helm charts, and Jenkins. Reproducible builds and reliable rollouts for services where regressions carry real cost.
On-device computer vision with OpenCV, embedded data collection, and sensor fusion feeding ML models — a proven pattern that saved $1M/yr via model inference detecting issues before they became costly.
Legacy SQL Server to MySQL/Postgres APIs. Stored procedure logic translated into well-tested service code that integrates cleanly with mobile and web clients.
Design and build automated testing pipelines and verification systems for software and safety-critical hardware: CI-integrated test suites, RF signals, serial protocols, timing, and acceptance review of contractor deliverables.
Software and test requirements documents, technical proposals, contractor deliverable specifications. Strong written communication is part of the deliverable.
Lead and mentor backend and mobile engineers, including outside contractors. Slot in as architect, scrum master, or senior individual contributor — whichever shape the engagement needs.
Targeted engagements such as test automation, ML pipeline design review, infrastructure debugging, performance analysis, or a focused build.
Fixed-scope delivery against defined acceptance criteria — for example, a streaming inference service, a data migration, or a test harness.
Ongoing staff-level capacity for teams that require senior infrastructure and AI judgment without a full-time hire.
Share a brief description of what you are building, where it is stuck, and your timeline. Replies within one business day.
