Principal AI Engineer

Principal AI Engineer — Velsera AI Platform & Enablement · Reports to the CTO · Senior individual contributor About the role Velsera builds software and infrastructure for precision medicine — research platforms, clinical and diagnostic applications, and the systems that keep them running in regulated environments. We are adding AI capability across that portfolio and inside our own operations: governed model access, self-hosted and managed LLM serving, evaluation and audit, and integration into the products and business processes people already depend on. This is a deliberately broad role. You will be deployed where the highest-value AI work is at the time — a customer-facing product capability in one quarter, an internal enterprise workflow in the next, a strategic account or funded program after that. The mandate stays the same wherever you land: design and ship production AI systems that hold up under real compliance requirements, work across AWS, Azure, and GCP, and leave behind reusable patterns rather than one-off builds. You will set technical direction for what is expected to grow into an AI platform and enablement team. What you'll work on Build a governed model access layer — self-hosted open-weight models, cloud-managed models (Bedrock, Vertex AI, Azure OpenAI), and customer- or partner-supplied models — designed to be consumed by more than one product or business function. Integrate AI capabilities into product experiences and enterprise workflows across batch, interactive, and agentic patterns. Establish the patterns everyone else reuses: evaluation, versioning, approvals, audit trails, cost control, guardrails, and safe rollout and rollback. Partner with product, engineering, security, QARA/compliance, IT, and scientific and commercial teams to introduce AI-native architectures that people can actually adopt. Move between assignments as business priorities shift, and make what you build in one part of the business usable in the next. What you'll deliver (first 6–12 months) A production-ready, compliant AI/LLM serving and invocation layer that at least two products or business functions adopt — multi-tenant, auditable, and secure. A model governance workflow (intake, evaluation, approval, versioning, deprecation) that satisfies both regulated customers and our own quality system. Two or three AI capabilities shipped end to end in different parts of the business — for example a customer-facing product feature, an internal process automation, and assisted validation or compliance tooling. Integration patterns that preserve reproducibility, traceability, and standards alignment wherever the work lands. Operational readiness: monitoring, evaluation harnesses, incident playbooks, cost visibility, and measurable SLOs for key AI services. A defensible internal point of view on where we should build, buy, or not use AI at all — backed by what you shipped. How we build (and what we'll expect you to…