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SG
SCAN Group
AI Engineering Manager
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What they do
An Artificial Intelligence Engineer develops, tests, and deploys artificial intelligence models. May work closely with data software engineers and data professionals to train and implement AI models into existing systems or develop new applications.
$160,491 / year median in California
Job Description
AI Engineering Manager SCAN Group
- 3.7 Long Beach, CA Job Details Full-time $125,400
- $215,975 a year 14 hours ago Benefits Wellness program Paid holidays Tuition reimbursement Paid time off 401(k) matching Qualifications RESTful API Cloud engineering team management Stakeholder relationship building Stakeholder management Full Job Description 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.
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.