Description
We're building a world of health around every individual , shaping a more connected, convenient and compassionate health experience.
As a hands-on software developer in AI Governance Engineering team, you will create software and AI Agents responsible for delivering governance-as-code: platform-agnostic SDKs, APIs, and automated controls that help teams move faster while ensuring responsible AI guardrails are consistently enforced.
Key Responsibilities
- Automated Use Case Intake & Risk Classification
+ Code and Design unified intake APIs and user experiences that reduce friction and automatically populate metadata and integrate with application registration and provisioning workflows + Implement dynamic intake flows for ML, Generative AI, and agentic use cases + Deliver automated inherent risk scoring and risk-based routing to provisioning and monitoring workflows
- Governance-as-Code Toolkits
+ Partner with AI Governance Data Scientist to develop a platform-agnostic Python/SDK offering automated governance controls such as bias, drift, and AI safety testing + Integrate governance checks into CI/CD pipelines so evidence is generated during development
- Enterprise Model & Use Case Registry Integration
+ Enable event-driven ingestion of intake data and model artifacts into the enterprise registry + Support automated generation of audit-ready compliance artifacts and APIs for reviewers and auditors
- Self-Service Enablement & Automation
+ Expand policy-based routing for low- and high-risk AI use cases + Automate proof-of-concept lifecycle patterns and accelerate access to approved AI environments
- Governance for Agentic Automation
+ Establish execution safety patterns for internal AI agents, including permission-scoped actions + Partner with information security to implement runtime guardrails such as prompt filtering, data protection controls, and real-time observability
Required Qualifications
- Experience developing software at scale
- Good knowledge of system architecture skills across APIs, automation, CI/CD, and data platforms
- Experience building developer-facing tools such as SDKs, templates, or automation frameworks
- Familiarity with ML and Generative AI lifecycles and how governance controls can be validated through automation
Preferred Qualifications
- Experience with Responsible AI concepts such as fairness, transparency, explainability, and auditability
- Experience integrating automated quality gates into Git-based workflows and CI/CD pipelines
- Background working in regulated or high-trust technology environments
- Ability to translate governance or risk requirements into scalable technical solutions
Education
- Bachelor's degree or equivalent work experience in Mathematics, Statistics, Computer Science, Analytics, Engineering, or related discipline. Master's degree preferred
What Success Looks Like
- AI intake processes are simpler, faster, and largely automated
- Engineering teams have reusable SDKs and templates that make responsible AI the default
- Validation of controls shifts from self-reporting to automated checks and evidence
- Centralized observability provides real-time insight into AI usage, performance, and risk
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