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About This Role
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 conversations.
- Strong business partnering skills, self\-direction, and influence; able to define roadmaps, persuade skeptics, and drive adoption of new processes or technologies.
- Demonstrates curiosity, initiative, and continuous learning while creating the systems, practices, and coaching that help the broader engineering team adapt to new frameworks and technologies.
- Leadership \- Develops others, sets clear expectations, and builds accountable engineering teams
- Business Insight \- Connects technology strategy to enterprise priorities, operational needs, and member impact
- Decision Quality \- Makes sound architectural and delivery tradeoffs under ambiguity
- Strategic Mindset \- Creates durable engineering strategies, standards, and roadmaps that scale beyond a single project
- Collaborates \- Engages effectively with peer leaders and cross\-functional partners to drive shared outcomes
- Software Development – Writes code and contributes to team projects for AI capabilities deployed in cloud environments.
Preferred Certifications or Licenses:
- Microsoft Certified: Azure AI Engineer Associate
- Relevant certifications in cloud platforms, AI engineering, machine learning, data engineering, or software development are beneficial.
- Additional certifications in Responsible AI, Data Governance, or Cybersecurity are advantageous.
Experience Preferred:
- Extensive experience (8\+ years) in software engineering, AI engineering, data engineering, machine learning, automation, or enterprise technology delivery, ideally within regulated industries such as healthcare.
- Demonstrated experience leading or managing engineers, including coaching, performance feedback, workload planning, technical mentorship, and team capability development.
- Proven ability to define and govern solution architecture for enterprise\-scale AI or digital transformation initiatives, including cross\-functional collaboration with business, healthcare, data, security, compliance, and technology teams.
- Experience establishing engineering practices for production\-grade applications, APIs, data pipelines, automation workflows, cloud\-based AI services, testing, documentation, release management, and operational support.
- Familiarity with Azure AI Foundry, Azure OpenAI, Azure AI Services, Databricks, Snowflake, or similar enterprise AI and data platforms is highly valued.
- Strong leader\-to\-leader communication and stakeholder management skills, with ability to translate enterprise priorities into technical direction, align peers, and drive adoption across diverse teams.
What's in it for you?
- Base Salary Range: $125,400 to $215,975 annually
- An annual employee bonus program
- Robust Wellness Program
- Generous paid\-time\-off (PTO)
- 11 paid holidays per year, 1 floating holiday, birthday off, and 2 volunteer days
- Excellent 401(k) Retirement Saving Plan with employer match
- Robust employee recognition program
- Tuition reimbursement
- An opportunity to become part of a team that makes a difference to our members and our community every day!
We're always looking for talented people to join our team! Qualified applicants are encouraged to apply now!
At SCAN we believe that it is our business to improve the state of our world. Each of us has a responsibility to drive Equality in our communities and workplaces. We are committed to creating a workforce that reflects our community through inclusive programs and initiatives such as equal pay, employee resource groups, inclusive benefits, and more.
SCAN is proud to be an Equal Employment Opportunity and Affirmative Action workplace. Individuals seeking employment will receive consideration for employment without regard to race, color, national origin, religion, age, sex (including pregnancy, childbirth or related medical conditions), sexual orientation, gender perception or identity, age, marital status, disability, protected veteran status or any other status protected by law. A background check is required.
\#LI\-JB1 \#LI\-Hybrid
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
*The contractor will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information. 41 CFR 60\-1\.35(c)*
Salary Context
This $125K-$215K range is in the lower quartile for AI Engineering Manager roles in our dataset (median: $185K across 13 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 4,317 AI roles we're tracking, AI Engineering Manager positions make up 0% of the market. At SCAN Health Plan, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Engineering Manager roles pay a median of $244,000 based on 23 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($170K) sits 30% below the category median. Disclosed range: $125K to $215K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
SCAN Health Plan AI Hiring
SCAN Health Plan has 3 open AI roles right now. They're hiring across AI Engineering Manager, AI Software Engineer, AI Architect. Positions span Long Beach, CA, US, Lakewood, CA, US. Compensation range: $182K - $254K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI Engineering Manager roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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