Principal AI Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop a governed model access layer for self-hosted open-weight models, cloud-managed models, and models provided by customers or partners.

• Incorporate AI functionalities into product experiences and enterprise workflows encompassing batch, interactive, and agentic patterns.

• Create reusable patterns for evaluation, versioning, approvals, audit trails, cost management, guardrails, and safe rollout/rollback processes.

• Collaborate with product, engineering, security, QARA/compliance, IT, scientific, and commercial teams to design AI-native architectures.

• Adapt to changing business priorities by moving between assignments and ensuring solutions are reusable across the organization.

• Deliver a production-ready, compliant, multi-tenant, auditable, and secure AI/LLM serving and invocation layer.

• Implement a model governance workflow that meets the needs of regulated customers and complies with Velsera's quality system.

• Launch two or three comprehensive AI capabilities across various business sectors.

• Develop integration patterns that support reproducibility, traceability, and adherence to standards.

• Set up monitoring systems, evaluation harnesses, incident playbooks, cost visibility, and measurable SLOs.

• Formulate an evidence-based perspective on whether to build, buy, or avoid AI solutions.

• Define the technical direction for the future AI platform and enablement team.

⛳️ Requirements

• Over 7 years of experience in software engineering, with at least 3 years dedicated to deploying AI/ML systems into production.

• Proficiency in Python and familiarity with either Java, Go, or TypeScript.

• Comfortable working within a polyglot codebase and reviewing production code.

• Hands-on experience with secure cloud architecture on at least one major cloud platform, including aspects like network isolation, IAM boundaries, private connectivity, and audit logging.

• Willingness to work across AWS, Azure, and GCP.

• Experience in operating or integrating self-hosted open-weight models, managed model APIs, and models provided by customers.

• Familiarity with MLOps/LLMOps using AWS Bedrock, Google Vertex AI, Azure AI Foundry, or similar platforms.

• Proven experience in establishing governance for ML/LLM systems, including managing evaluation, versioning, approvals, rollout, rollback, and deprecation.

• Background in designing systems for regulated or audited environments such as HIPAA, 21 CFR Part 11, GxP, FedRAMP, SOC 2, GDPR, or similar standards.

• Experience with RAG and LLM tool-use or agentic patterns beyond prototypes, including evaluation processes.

• A successful track record of integrating with existing products, third-party SaaS, and enterprise data sources.

• Excellent written communication skills for diverse audiences, including engineering, product, security, compliance, business, and scientific stakeholders.

• Ability to switch contexts across various problem domains and navigate ambiguous requirements.

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

• Familiarity with ISO 13485, IEC 62304, IVDR, clinical/diagnostic contexts, CWL/WDL/Nextflow, GA4GH, FHIR, OMOP, enterprise systems integration, multi-cloud production, or customer-/grant-funded projects is also a plus.

🏝️ Benefits

• A collaborative and supportive work environment.

• Flexible working arrangements.

• A team that values inclusivity and diversity.

• Equal opportunity employment policies.

• Opportunities to engage in groundbreaking science and technology innovations.

• A chance to influence AI across products and company operations.

• Wide scope of ownership in projects.

• Work that has a meaningful impact on patient lives and the healthcare industry.