Sr. MLOps & Data Platform Engineer
Location: remote is good but need to cover PST time
Duration: 1 year
ML Ops & Data Platform Engineer — Contractor Overview We are seeking an experienced ML Ops & Data Platform Engineer to join a team for projects covering two areas of focus: 1. ML Operations — help design, build, and operate production-grade machine learning infrastructure on AWS for advanced cancer screening and precision oncology applications, and support the integration of AI/ML into existing and new workloads. 2. Data Platforming — contribute to the establishment and creation of a shared Data Platform for use across the broader Science Office: a governed, self-service data foundation (ingestion, lakehouse architecture, cataloging, quality, and access control) serving multiple science teams and use cases, of which ML workloads are one consumer among many. These two workstreams are complementary, not parallel silos — the data platform is the foundation the ML pipelines will increasingly consume from. This role is platform-first: as a senior hands-on contributor working alongside internal data science, engineering, and broader Science Office teams, this person will drive key pieces of the AWS architecture, automation, and operational reliability of both the ML pipelines and the underlying data platform, sharing accountability for these systems with the rest of the team. Deliverables include working infrastructure-as-code, CI/CD pipelines, data ingestion pipelines, catalog/governance set up, observability, and documentation/knowledge transfer to internal teams as engagements close.