Principal AI Engineer — Velsera

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 the 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)

• 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.

Requirements

Must-Haves

• 7+ years in software engineering, including 3+ years shipping AI/ML systems to production.

• Strong Python, plus one of Java, Go, or TypeScript; comfortable in a polyglot codebase and in production code review.

• Hands-on experience with secure cloud architecture on at least one major cloud — network isolation, IAM boundaries, private connectivity, audit logging — and readiness to work in the others.

• Experience operating or integrating model serving across delivery modes: self-hosted open-weight models, managed model APIs, and customer-provided models.

• MLOps/LLMOps experience with tooling such as AWS Bedrock, Google Vertex AI, Azure AI Foundry, or equivalent.

• Built governance for ML/LLM systems: evaluation, versioning, approvals, rollout and rollback, deprecation.

• Comfortable designing for regulated or audited environments (HIPAA, 21 CFR Part 11, GxP, FedRAMP, SOC 2, GDPR, or similar).

• Experience with RAG and LLM tool-use/agentic patterns beyond prototypes, including how you evaluated them.

• Track record integrating with systems you don't own — existing products, third-party SaaS, enterprise data sources — without breaking them.

• Clear written communication for mixed audiences: engineering, product, security and compliance, business stakeholders, and scientists.

• Comfort switching context across problem domains and starting from ambiguous requirements.

Nice-to-Haves

• Experience in genomics, biomedical data, or life sciences platforms.

• Software built under a quality management system (ISO 13485, IEC 62304, IVDR) or in clinical/diagnostic contexts.

• Integrating AI capabilities into workflow or orchestration engines (CWL/WDL/Nextflow or similar).

• Familiarity with GA4GH standards (WES/DRS/TRS) and/or clinical data models (FHIR, OMOP).

• Enterprise systems integration: CRM, ERP, ITSM, document and quality management, collaboration suites.

• Production experience on more than one cloud, with pragmatic multi-cloud trade-off judgment.

• Experience on customer-funded or grant-funded engagements alongside product and services teams.

This Role Is a Fit If...

• You want to build the platform layer and the capabilities on top of it — not model research or prompt engineering alone.

• You would rather solve four different problems well across a business than the same problem for three years.

• You are energized rather than frustrated by environments where auditability, access control, and traceability are hard requirements.

• You like improving live production systems without breaking what customers and colleagues rely on.

• You are willing to say no to AI when it is not the right tool, and to say so in writing.

Why Join

You'll shape how AI gets built across Velsera — in the products our customers run sensitive biomedical data through, and in the way we operate as a company. The constraints are real, the users are close, and the scope of ownership is unusually broad for a single role.