Senior AI Engineer

About Velsera Medicine moves too slow. At Velsera, we are changing that. Velsera was formed in 2023 through the shared vision of Seven Bridges and Pierian, with a mission to accelerate the discovery, development, and delivery of life-changing insights. Velsera provides software and professional services for: AI-powered multimodal data harmonization and analytics for drug discovery and development IVD development, validation, and regulatory approval Clinical NGS interpretation, reporting, and adoption With our headquarters in Boston, MA, we are growing and expanding our teams located in different countries! About the role Velsera builds and operates precision medicine software across the Seven Bridges Platform (SBP), Clinical Genomics Workspace (CGW), and FastFinder. Our teams support genomic researchers, clinical labs, and pharma customers—often in highly regulated environments. In this role, you’ll build the internal AI platform that helps engineers ship and operate software faster and more reliably: AI-assisted developer workflows (coding, testing, review, docs) and AI-assisted operations (triage, automation, and self-healing patterns). You’ll partner closely with our Cloud Architect (who owns observability signal quality) and report to the CTO. This is a shared platform role serving multiple engineering teams—not an embedded role on a single product team. What will you do? You’ll deliver practical AI capabilities on top of our existing engineering and operations foundation (CI/CD, code quality, and observability) with strong guardrails for HIPAA/GxP and FedRAMP-boundary work. Tech environment (varies by team): Java, Python, Go, TypeScript; AWS; GitLab/Jenkins/GitLab CI; SonarQube; Prometheus + ELK and AWS-native monitoring. Build and operate an internal AI platform for developer workflows: code assistance, PR review support, test generation, documentation, and automation that fits how our teams work. Integrate AI capabilities into the toolchain (CI/CD, code quality, repos) and make adoption easy through templates, examples, and self-service patterns. Implement AI quality gates (policy checks, confidence thresholds, regression tests, secure-by-default configs) so AI-generated changes cannot be merged/released unless they meet standards. Design for regulated environments: implement logging/auditability, access controls, and data-handling guardrails for HIPAA/GxP work. Maintain a FedRAMP-boundary-safe approach for SBP Federal work (separation where required; evaluate compliant alternatives; prevent prohibited tooling/data flows). Partner with engineering leads and security/compliance to define and roll out internal standards for responsible AI use (including review practices and acceptable-use guidance). Build AI-assisted operations on top of existing observability signals: anomaly detection, incident triage support, runbook-driven automation, and self-healing patterns to reduce MTTR. Measure impact (adoption, cycle time, incident metrics), iterate quickly, and communicate progress clearly to engineering leadership.