Principal AI Engineer
The posting does not state a salary. This range is our estimate from the role, the location and the stack — treat it as a guide, not an offer.
- Level
- Principal
- Apply from
- Apply from anywhere
About the role
- Lead AI initiatives across multiple products.
- Design compliant AI systems for precision medicine.
- Integrate AI into workflows and product experiences.
- Establish reusable patterns for AI governance.
- Collaborate with cross-functional teams for AI adoption.
Skills the posting asks for
- AI
- AWS
- Azure
- GCP
- LLM
Summarised from the employer’s posting, which is reproduced in full below.
The employer’s full posting
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. Ship the smallest thing that genuinely works, then harden it. Governance that makes a workflow unusable has failed.