AIML Engineering Manager

Information Technology Services (ITS) at the MCCCD is looking for a highly skilled and hands-on Manager of AI/ML Engineering, to serve as both a technical leader and people manager for the District's AI/ML engineering function. This role is responsible for designing, deploying, scaling, and supporting production-ready AI and machine learning solutions that advance student success, operational efficiency, workforce readiness, institutional effectiveness, and responsible AI adoption across the District.

This position combines technical depth, engineering leadership, and team management. The Manager of AI/ML Engineering, will lead a team of AI/ML engineers, developers, analysts, and related technical staff while remaining actively involved in solution architecture, model deployment, AI application integration, workflow orchestration, and operational support. The role will work closely with data engineering, architecture, governance, cybersecurity, analytics, and business stakeholders across academic, student, administrative, and operational domains.

The ideal candidate is a strong technical builder and practical leader who can establish engineering standards, mentor team members, and deliver scalable, secure, and reliable AI solutions in a complex enterprise environment.

Essential Functions

20% - AI/ML Engineering and Solution Delivery

Design, build, test, deploy, and maintain enterprise AI and machine learning solutions that support reporting, automation, decision support, intelligent search, workflow optimization, and user productivity.

Operationalize predictive models, natural language processing solutions, retrieval-based systems, recommendation workflows, classification services, and generative AI-enabled applications.

Build reusable AI/ML engineering patterns, services, and components to improve scalability, consistency, and speed of delivery.

Integrate AI/ML capabilities with enterprise data platforms, business applications, APIs, and workflow systems.

Support both real-time and batch-based inference and processing patterns as appropriate.

20% - Cloud and Platform Engineering

Build and maintain AI/ML solutions using AWS as the primary cloud platform.

Develop and support workflows using AWS services such as SageMaker, S3, Lambda, Bedrock, IAM, CloudWatch, Step Functions, API Gateway, ECS/EKS, and related technologies as appropriate.

Apply best practices for performance optimization, monitoring, logging, access control, resilience, model lifecycle management, and cost awareness in AWS-based environments.

Support modernization of manual, rule-based, or legacy workflows into scalable AI-enabled services.

20% - Governance, Security, and Responsible AI

Ensure AI/ML solutions comply with District governance, privacy, security, and access-control requirements.

Support responsible AI practices by incorporating guardrails, human review, prompt controls, output monitoring, and safe handling of sensitive data.

Work closely with governance, legal, compliance, and cybersecurity stakeholders to align AI implementations with District policy and institutional expectations.

Contribute to trustworthy and well-governed AI adoption that supports institutional decision-making and operational efficiency.

20% - Team Leadership and People Management

Supervise, mentor, and develop AI/ML engineers, AI developers, or related technical staff.

Assign work, establish priorities, provide technical coaching, and support employee development.

Conduct code reviews, design reviews, model/solution reviews, and technical guidance to ensure quality and consistency across the team.

Build a high-performing engineering culture focused on accountability, experimentation, responsible AI use, documentation, collaboration, and continuous improvement.

Help identify staffing, skill development, tooling, and process needs for a growing AI engineering function.

10% - Production Reliability and Operational Excellence

Establish and enforce standards for development, testing, deployment, release management, monitoring, documentation, and supportability of AI/ML systems.

Improve engineering practices related to CI/CD, MLOps, LLMOps, versioning, observability, model evaluation, prompt/version control, and issue resolution.

Ensure AI solutions include appropriate controls for output quality, traceability, auditability, human oversight, and operational support.

Lead troubleshooting of model failures, workflow breakdowns, hallucination or output quality issues, prompt regressions, latency bottlenecks, and production incidents, and drive root-cause resolution.

10% - Cross-Functional Collaboration

Partner with data architects, data engineers, governance leaders, analysts, institutional research teams, application teams, and business stakeholders to translate requirements into scalable AI solutions.

Work with technical and functional teams to identify and prioritize high-value use cases and implementation opportunities.

Participate in roadmap planning, solution estimation, prioritization, vendor/tool evaluation, and technical design discussions.

Communicate technical tradeoffs, risks, and implementation approaches clearly to both technical and non-technical audiences.

Performs related duties as assigned