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Tech Stack
Tools & technologiesAWSCloudGoJavaPythonTypeScript
About the role
Key responsibilities & impact- Build a governed model access layer (self-hosted open-weight models, cloud-managed models such as Bedrock, and customer-supplied models)
- Integrate AI capabilities into platform experiences (batch workflows and interactive sessions)
- Establish patterns for evaluation, versioning, approvals, audit trails, and safe rollout
- Partner with product, security/compliance, and scientific teams to introduce AI-native architectures and ship capabilities customers can adopt
- A production-ready, compliant AI/LLM serving and invocation layer for Seven Bridges (multi-tenant, auditable, and secure)
- A clear governance workflow for models (intake, evaluation, approval, versioning, deprecation) that works for regulated customers
- A first set of “AI in the platform” features shipped end-to-end (e.g., assisted validation/compliance tooling, cost/error assistance, workflow helpers)
- Integration patterns that keep workflows reproducible and standards-aligned (CWL/WDL/Nextflow and GA4GH-friendly where applicable)
- Operational readiness: monitoring, incident playbooks, and measurable SLOs for key AI services
Requirements
What you’ll need- 7+ years in software engineering, including 3+ years shipping AI/ML systems to production
- Strong Python, plus one of Java/Go/TypeScript; comfortable in a polyglot codebase and production code reviews
- 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)
Benefits
Comp & perks- Flexible Work & Time Off - Embrace hybrid work models and enjoy the freedom of unlimited paid time off to support work-life balance.
- Health & Well-being - Access comprehensive group medical and life insurance coverage, along with a 24/7 Employee Assistance Program (EAP) for mental health and wellness support.
- Growth & Learning - Fuel your professional journey with continuous learning and development programs designed to help you upskill and grow.
- Engaging & Fun Work Culture - Experience a vibrant workplace with team events, celebrations, and engaging activities that make every workday enjoyable.
- & Many More...
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
PythonJavaGoTypeScriptMLOpsmodel servingAI/ML systemscloud architecturesgovernance for ML/LLM systemsevaluation and versioning
Soft Skills
clear written communicationcollaborationproblem-solvingdesigning for regulated environments
Certifications
FedRAMPHIPAA21 CFR Part 11GxP