Velsera

biomedical

PrincipalAIEngineer

Pune, Maharashtra, India FULL TIME
The Brief

“Principal AI Engineer at Velsera. Skills: AI platform layer development, Production AI systems design and shipping, Compliance-focused AI architecture, Multi-cloud AI strategy, MLOps, LLM serving and governance. Design and ship production AI systems that meet compliance needs. Build a governed model access layer”

What You'll Achieve.

Accelerate the discovery, development, and delivery of life-changing insights; Ship production AI systems that meet compliance needs; Ship capabilities customers can adopt; Improve the existing production platform without breaking customer reliance

Industry & Context.

biomedical

What They're Looking For.

Must Have

7+ years in software engineering, 3+ years shipping AI/ML systems to production, Python, Java/Go/TypeScript, Hands-on experience with secure cloud architectures on AWS (network isolation, IAM boundaries, private connectivity, audit logging), Experience operating or integrating model serving across options: self-hosted open-weight models, managed model APIs (e.g., Bedrock), and customer-provided models, MLOps experience using AWS Bedrock, Google Vertex AI or similar, Built governance for ML/LLM systems (evaluation, versioning, approvals, rollout/rollback, deprecation), Comfortable designing for regulated environments (FedRAMP, HIPAA, 21 CFR Part 11, GxP, or similar), Experience with RAG and LLM tool-use/agentic patterns beyond prototypes, Clear written communication for mixed audiences (engineering, product, security/compliance, and scientists)

Nice to Have

Experience in genomics, biomedical data, or life sciences platforms, Integrating AI capabilities into workflow engines (CWL/WDL/Nextflow) or similar orchestration systems, Familiarity with GA4GH standards (e.g., WES/DRS/TRS) and/or clinical data models (FHIR/OMOP), Production experience on both AWS and comfort making pragmatic multi-cloud trade-offs

What You'll Do.

Design and ship production AI systems that meet compliance needs

Build a governed model access layer

Integrate AI capabilities into platform experiences

Establish patterns for evaluation

and scientific teams to introduce AI-native architectures and ship capabilities customers can adopt

Build a production-ready

compliant AI/LLM serving and invocation layer for Seven Bridges

Build a clear governance workflow for models

Build a first set of 'AI in the platform' features shipped end-to-end

Build integration patterns that keep workflows reproducible and standards-aligned

Ensure operational readiness: monitoring

and measurable SLOs for key AI services

How You'll Work.

Team & Collaboration

Partner with product, security/compliance, and scientific teams

Communication Scope

Clear written communication for mixed audiences (engineering, product, security/compliance, and scientists)

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