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 optimize for) You will make trade-offs in an environment that is multi-product, multi-cloud, standards-driven, and compliance-heavy. A few things matter a lot here: - Reusability over one-offs. Design the second and third use case into the first one. A solution that only works for one product or one team is a partial solution. - Standards and clean interfaces. Prefer open standards and well-defined boundaries over bespoke integrations. - Multi-cloud, multi-deployment reality. AWS, Azure, and GCP are all in play, alongside customer-managed and self-hosted environments. Avoid hard dependencies on a single provider's AI stack. - Security and auditability by default. Access control, logging, traceability, and data governance are part of the design, not add-ons. - Reproducibility. AI features have to fit into workflows and processes that need to be repeatable and explainable, sometimes years later. - Proportion.