Senior AI Engineer

MiniMed•Atlanta, GA

About The Position

MiniMed is seeking a hands-on Senior AI Engineer for its lean AI and Data Science team. The role focuses on taking AI/ML models and LLM-powered agents from rapid prototypes through reliable, auditable production deployment in a regulated medical-device environment. The engineer owns a capability from prototype through its initial production release and first monitoring cycle, with success measured by adoption and business value, before handing it off to Model Operations for steady-state support. Approximately one-fifth of the role involves reviewing and quality-gating other engineers’ designs and establishing reusable technical patterns. The role works with the MIA stack, including Databricks, LangGraph/LangSmith, and enterprise agent platforms. It also involves integrating models and agents with enterprise APIs, identity and access systems, systems of record such as Salesforce and SAP, and enterprise data pipelines. The engineer partners with the Product Manager, AI & Data Science to select work and pursue outcomes independently, while upholding safety, privacy, compliance, auditability, and human-in-the-loop requirements where applicable. The position requires a bachelor’s degree and at least four years of relevant experience, or an advanced degree and at least two years of relevant experience. The salary range is $112,000–$190,000 for U.S. locations excluding Puerto Rico, and $124,000–$212,000 for specified high-cost locations. The position is eligible for a short-term incentive. Regular employees working at least 20 hours per week are eligible for the benefits described in the posting.

Requirements

Bachelor’s degree and a minimum of 4 years of relevant experience, or an advanced degree and a minimum of 2 years of relevant experience.

Nice To Haves

Demonstrated delivery of LLM-powered systems or ML models to production, beyond prototypes, evaluations, or internal demos.
Strong system design skills and end-to-end ownership from prototype through production deployment.
Experience technically leading or directing other engineers, including reviewing their work.
Strong stakeholder-facing communication, including scoping ambiguous problems with business partners and explaining technical trade-offs such as latency, cost, and model risk to non-technical leaders.
Hands-on experience with Databricks, LangGraph, LangSmith, vector databases, and managed RAG/retrieval.
Forward-deployed or embedded engineering experience building AI systems using real business data, workflows, and constraints.
Experience integrating AI or software systems into complex enterprise environments, including APIs, identity and authentication, systems of record such as Salesforce or SAP, and data pipelines.
Comfort operating within a formal MLOps discipline, including a defined handoff to a model operations function.
Experience in a regulated industry such as medical devices, healthcare, or financial services; familiarity with HIPAA, audit, and validation requirements.

Responsibilities

Prototype AI/ML models, agents, and tools against real MiniMed business problems using actual business data and workflows.
Build, evaluate, and deploy solutions on the MIA stack, using evaluation-driven development, guardrails, and deployment patterns that support reliability, auditability, and maintainability.
Own capabilities from prototype through initial production release and the first monitoring cycle, drive adoption and business value, then hand off steady-state operations to Model Operations.
Integrate agents and models with APIs, identity and access systems such as SSO/SAML/OAuth, systems of record such as Salesforce and SAP, and enterprise data pipelines.
Establish reusable patterns for agent design, tool-calling, retrieval, and evaluation harnesses.
Review and quality-gate other engineers’ work and designs.
Feed field learnings into improvements to the MIA platform, shared tooling, and reusable-pattern roadmap.
Partner with the Product Manager, AI & Data Science to choose what to build and when to stop, working independently toward outcomes.
Uphold safety, privacy, and compliance requirements for a medical-device context, including auditability and human-in-the-loop practices where required.

Benefits

Competitive salary and flexible benefits package.
Health insurance.
Dental insurance.
Vision insurance.
Health Savings Account.
Healthcare Flexible Spending Account.
Life insurance.
Long-term disability leave.
Dependent daycare spending account.
Incentive plans, including eligibility for a short-term incentive.
401(k) plan with company match.
Short-term disability coverage.
Paid time off and holidays.
Employee Stock Purchase Plan.
Employee Assistance Program.
Eligible employees may also have access to a Non-qualified Retirement Plan Supplement and Capital Accumulation Plan, subject to IRS minimum earnings requirements.