AI Engineering Manager

Founded in 1977 as the Senior Care Action Network, SCAN began with a simple but radical idea: that older adults deserve to stay healthy and independent. That belief was championed by a group of community activists we still honor today as the “12 Angry Seniors.” Their mission continues to guide everything we do.

Today, SCAN is a nonprofit health organization serving more than 500,000 people across Arizona, California, Nevada, New Mexico, Texas, and Washington, with over $8 billion in annual revenue. With nearly five decades of experience, we have built a distinctive, values-driven platform dedicated to improving care for older adults.

Our work spans Medicare Advantage, fully integrated care models, primary care, care for the most medically and socially complex populations, and next-generation care delivery models. Across all of this, we are united by a shared commitment: combining compassion with discipline, innovation with stewardship, and growth with integrity.

At SCAN, we believe scale should strengthen—not dilute—our mission. We are building the future of care for older adults, grounded in purpose, accountability, and respect for the people and communities we serve.

The Job

As an AI Engineering Manager, you’re the hands-on leader for our organization’s architecture, delivery, and team development. Your team builds enterprise AI solutions that improve how the organization operates, makes decisions, and serves its members.

This role is accountable for demonstrating and setting technical direction, leveling up engineering practices and the engineers. You will represent our team by partnering across business, data, security, and technology functions. You will ensure AI products are thoughtfully designed, responsibly governed, securely delivered, operationally reliable, and aligned to enterprise priorities.

Essential Job Functions

Hands-on Technical Contribution

Lead by example, contributing to our core products and participating in development activities
Develop prototypes and showcase new capabilities available on our cloud platforms as they become available
Expand our AI platform by owning functional expansions (sample areas include Agent Orchestration, Knowledge, Skills, and Data Integrations)

Architecture Leadership & Technical Direction

Define and steward enterprise-grade AI platform/solution architectures, ensuring systems are scalable, secure, maintainable, and aligned to emerging technology frameworks.
Set technical standards, design principles, and decision frameworks for AI applications, data integrations, agentic workflows, and production platforms.
Guide architectural tradeoff decisions across speed, quality, risk, cost, reusability, and long-term operability.

Engineering Discipline & Delivery Management

Establish disciplined engineering practices for requirements definition, estimation, solution design, code quality, testing, documentation, release management, and production readiness.
Lead teams through SCAN’s SAFe Agile delivery rhythms, ensuring technical work is prioritized, sequenced, communicated, and completed with appropriate rigor.
Drive continuous improvement in engineering processes, delivery predictability, observability, supportability, and operational excellence.

People Leadership & Engineer Development

Coach, mentor, and develop engineers; set clear expectations, provide actionable feedback, grow technical judgment, and increase ownership over time.
Build team capability in AI engineering, cloud software development, architecture, security, responsible AI, and enterprise delivery practices.
Create a high-accountability team culture that values curiosity, clarity, craftsmanship, collaboration, and responsible innovation.

Enterprise Partnership & Leader-to-Leader Engagement

Engage senior leaders to translate enterprise priorities into executable AI product and platform roadmaps.
Represent engineering perspectives in cross-functional planning, governance, risk discussions, and prioritization forums.
Influence stakeholders through clear communication, sound judgment, practical tradeoff analysis, and shared accountability for business outcomes.

Responsible AI, Security, Governance & Operational Accountability

Ensure AI systems are designed and delivered with responsible AI practices, including transparency, traceability, explainability, fairness, privacy, security, and human oversight.
Partner with security, compliance, legal, governance, and platform teams to embed appropriate controls into architecture and delivery processes.
Own engineering accountability for solution documentation, risk awareness, production readiness, operational support, and continuous improvement.

Your Qualifications

Bachelor's Degree or equivalent experience in Computer science, Engineering, or a related field required
Master’s Degree in Computer science, Engineering, or a related field preferred
Advanced ability to define, evaluate, and govern AI solution architectures, including LLM-based systems, RAG, vector embeddings, agent orchestration, data integration, and platform patterns.
Ability to lead engineering teams through disciplined delivery practices, including design reviews, coding standards, testing strategy, documentation, CI/CD, observability, release readiness, and support models.
Proficiency with Azure AI Services, Azure AI Foundry, enterprise cloud data platforms (Databricks and Snowflake), and integration with platforms such as Microsoft 365.
Strong technical fluency in Python or equivalent languages, REST APIs, ML/LLMOps tooling, and modern application frameworks sufficient to guide architectural decisions and coach engineers.
Ability to coach engineers, build technical judgment, delegate effectively, provide actionable feedback, and grow ownership and accountability within the team.
Leader-to-leader communication skills, with ability to engage executives, peer leaders, and cross-functional partners in prioritization, tradeoff, governance, and adoption con