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Principal AI Engineer
USA-MiniMed Distribution Corp. 1018
30 minutes ago
Full-time
On-site
Atlanta, Georgia, United States
We anticipate the application window for this opening will close on - 23 Nov 2026
At MiniMed, you can begin a lifelong career of exploration and innovation, while helping make a difference in the lives of people living with diabetes around the globe. You'll lead with purpose, breaking down barriers to innovation for a more connected, compassionate world.
About the Role
MiniMed is building a lean, high-leverage AI & Data Science team. We are looking for a Principal AI Engineer to be our senior technical anchor - the person who builds AI capabilities hands-on and sets the standard the rest of the team builds to. This is a builder's role first. You will take AI/ML models and LLM-powered agents from a fast "proof of life" prototype through to a production deployment that holds up in a regulated environment. Roughly a fifth of your time goes to the force-multiplier work: reviewing and quality-gating other engineers' designs, and establishing the reusable patterns that keep the architecture and the hard-won judgment in-house. To be clear about what this is not: this is not a people-management role, and it is not a role where you hand a notebook to someone else to productionize. You own the capability until it is live and delivering value. Responsibilities may include the following and other duties may be assigned. Prototype fast. Demonstrate "proof of life" for AI/ML models, agents, and tools against real MiniMed business problems, working from the business's actual data and workflows rather than a sanitized sandbox. Take it to production and drive adoption. Build, evaluate, and deploy on the MIA stack (Databricks, LangGraph/LangSmith, enterprise agent platforms), applying evaluation-driven development, guardrails, and deployment patterns so what ships is reliable, auditable, and maintainable. You own the capability from prototype through initial production release and its first monitoring cycle - measured by real adoption and business value, not just a stable deployment - then hand off to Model Operations for steady-state run. Integrate into the enterprise environment. Wire agents and models into the surrounding systems - APIs, identity and access (SSO/SAML/OAuth), systems of record such as Salesforce and SAP, and enterprise data pipelines - so capabilities work against real, messy production systems rather than in isolation. Set the technical standard. Establish reusable patterns for agent design, tool-calling, retrieval, and evaluation harnesses; review and quality-gate the work of other engineers on the team so the bar holds without a manager in the loop. Feed learnings back to the platform. Turn what you learn in the field into improvements to the MIA platform, shared tooling, and reusable‑pattern roadmap, so each deployment makes the next one faster. Self-direct against outcomes. Partner with the Product Manager, AI & Data Science to choose what to build and when to stop, without needing the problem pre‑decomposed for you. Build for a regulated environment. Uphold the safety, privacy, and compliance requirements of a medical‑device context, including auditability and human‑in‑the‑loop where required. Must Have: Minimum Requirements
Bachelor's degree in Computer Science, Engineering, or a related technical field and 8+ years of of relevant experience or advanced degree with a minimum of 6+ years of relevant experience. Nice to Have
Demonstrated delivery of LLM-powered systems or ML models to production - not prototypes, evaluations, or internal demos alone. 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 - able to scope ambiguous problems with business partners and explain technical trade‑offs (latency, cost, model risk) to non‑technical leaders. Hands‑on with Databricks, LangGraph, LangSmith, vector databases, and managed RAG/retrieval. Forward‑deployed or embedded engineering experience - building AI systems from inside the business's real data, workflows, and constraints. Experience integrating AI or software systems into complex enterprise environments - APIs, identity/auth, systems of record (e.g., Salesforce, 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 (medical device, healthcare, financial services); familiar‑with HIPAA, audit, and validation requirements. Physical Job Requirements
The above statements are intended to describe the general nature and level of work being performed by employees assigned to this position, but they are not an exhaustive list of all the required responsibilities and skills of this position. The physical demands described within the Responsibilities section of this job description are representative of those that must be met by an employee t
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MiniMed is building a lean, high-leverage AI & Data Science team. We are looking for a Principal AI Engineer to be our senior technical anchor - the person who builds AI capabilities hands-on and sets the standard the rest of the team builds to. This is a builder's role first. You will take AI/ML models and LLM-powered agents from a fast "proof of life" prototype through to a production deployment that holds up in a regulated environment. Roughly a fifth of your time goes to the force-multiplier work: reviewing and quality-gating other engineers' designs, and establishing the reusable patterns that keep the architecture and the hard-won judgment in-house. To be clear about what this is not: this is not a people-management role, and it is not a role where you hand a notebook to someone else to productionize. You own the capability until it is live and delivering value. Responsibilities may include the following and other duties may be assigned. Prototype fast. Demonstrate "proof of life" for AI/ML models, agents, and tools against real MiniMed business problems, working from the business's actual data and workflows rather than a sanitized sandbox. Take it to production and drive adoption. Build, evaluate, and deploy on the MIA stack (Databricks, LangGraph/LangSmith, enterprise agent platforms), applying evaluation-driven development, guardrails, and deployment patterns so what ships is reliable, auditable, and maintainable. You own the capability from prototype through initial production release and its first monitoring cycle - measured by real adoption and business value, not just a stable deployment - then hand off to Model Operations for steady-state run. Integrate into the enterprise environment. Wire agents and models into the surrounding systems - APIs, identity and access (SSO/SAML/OAuth), systems of record such as Salesforce and SAP, and enterprise data pipelines - so capabilities work against real, messy production systems rather than in isolation. Set the technical standard. Establish reusable patterns for agent design, tool-calling, retrieval, and evaluation harnesses; review and quality-gate the work of other engineers on the team so the bar holds without a manager in the loop. Feed learnings back to the platform. Turn what you learn in the field into improvements to the MIA platform, shared tooling, and reusable‑pattern roadmap, so each deployment makes the next one faster. Self-direct against outcomes. Partner with the Product Manager, AI & Data Science to choose what to build and when to stop, without needing the problem pre‑decomposed for you. Build for a regulated environment. Uphold the safety, privacy, and compliance requirements of a medical‑device context, including auditability and human‑in‑the‑loop where required. Must Have: Minimum Requirements
Bachelor's degree in Computer Science, Engineering, or a related technical field and 8+ years of of relevant experience or advanced degree with a minimum of 6+ years of relevant experience. Nice to Have
Demonstrated delivery of LLM-powered systems or ML models to production - not prototypes, evaluations, or internal demos alone. 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 - able to scope ambiguous problems with business partners and explain technical trade‑offs (latency, cost, model risk) to non‑technical leaders. Hands‑on with Databricks, LangGraph, LangSmith, vector databases, and managed RAG/retrieval. Forward‑deployed or embedded engineering experience - building AI systems from inside the business's real data, workflows, and constraints. Experience integrating AI or software systems into complex enterprise environments - APIs, identity/auth, systems of record (e.g., Salesforce, 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 (medical device, healthcare, financial services); familiar‑with HIPAA, audit, and validation requirements. Physical Job Requirements
The above statements are intended to describe the general nature and level of work being performed by employees assigned to this position, but they are not an exhaustive list of all the required responsibilities and skills of this position. The physical demands described within the Responsibilities section of this job description are representative of those that must be met by an employee t
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