Senior AI Engineer

## Senior AI Engineer Apply:
Flexible:
Atlanta, Georgia, United States of America:
USA-CA:
Full time:
Posted Today:
End Date:
November 11, 2026 (4 days left to apply):
R-802408

We anticipate the application window for this opening will close on - 11 Nov 2026

At Mini Med, 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
** Mini Med is building a lean, high-leverage AI & Data Science team. We are looking for a Senior AI Engineer 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.  

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 Mini Med 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, Lang Graph/Lang Smith, 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.
** Required Knowledge and

Experience:

** Requires a Bachelor's degree and minimum of 4 years of relevant experience, or advanced degree with a minimum of 2 years 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,…