Manager, Full-Stack Engineering, SMAI

Key Responsibilities

Organizational Leadership

• Lead, mentor, and inspire a team of Full-Stack AI Engineers responsible for end-to-end solution delivery.
• Build a culture centered around innovation, customer focus, operational excellence, engineering quality, and accountability.
• Drive workforce planning, talent acquisition, onboarding, employee development, succession planning, and performance management.
• Foster adoption of AI-first development practices, Agentic Coding, AI-assisted software engineering, and automation-driven productivity.
• Develop future technical leaders and create growth opportunities for engineering talent across SMAI.
• Establish engineering best practices, technical standards, and continuous learning programs.

Full-Stack AI Solution Leadership

• Lead development and deployment of AI-powered enterprise platforms supporting manufacturing, planning, quality, productivity, and operational intelligence.
• Drive end-to-end ownership across:
  • User Experience & Front-End Engineering
  • Application Development & APIs
  • Cloud Engineering
  • Data Engineering & Analytics
  • Machine Learning
  • Generative AI
  • Computer Vision
  • AI Operations (AIOps/MLOps)
  • Platform Engineering
• Partner with Product Owners, Architects, AI Scientists, and business stakeholders to define technology roadmaps and execution strategies.
• Ensure successful transition of solutions from proof-of-concept to resilient enterprise-scale deployments.
• Drive enterprise-wide adoption of reusable AI frameworks, engineering accelerators, and standardized development patterns.

AI, Computer Vision & Generative AI Leadership

• Lead engineering teams delivering:
  • Machine Learning Solutions
  • Predictive Analytics Platforms
  • Computer Vision Applications
  • Real-Time Monitoring Systems
  • Intelligent Automation Solutions
  • Generative AI Products
  • Autonomous Workflow Platforms
• Enable enterprise AI capabilities including:
  • Retrieval-Augmented Generation (RAG)
  • Agentic AI Systems
  • Vector Databases
  • LLM Integrations
  • Enterprise Knowledge Platforms
  • Multi-Agent Solutions
  • Physical AI and Autonomous Systems
• Drive adoption of Responsible AI principles including governance, transparency, explainability, compliance, security, and risk management.
• Collaborate with AI Platform, MLOps, and Architecture teams to operationalize AI solutions at scale.

Cloud Platform & Engineering Leadership

• Provide technical leadership across cloud-native technologies including:
  • Google Cloud Platform (GCP)
  • Snowflake
  • OpenShift
  • Kubernetes
  • Enterprise Integration Platforms
  • Streaming Data Technologies
• Drive architecture, scalability, resiliency, security, and operational excellence for enterprise cloud platforms.
• Lead implementation of:
  • Infrastructure as Code (IaC)
  • DevSecOps
  • CI/CD Pipelines
  • Platform Engineering
  • Cloud Automation
  • Observability and Monitoring Platforms
• Govern cloud platform utilization, cost optimization, reliability, and performance.

Real-Time Analytics, Image & Video Intelligence

• Lead development of advanced AI-powered solutions for:
  • Image Analytics
  • Video Intelligence
  • Digital Inspection
  • Defect Detection
  • Manufacturing Automation
  • Edge AI Applications
• Architect low-latency, highly scalable systems capable of processing:
  • Streaming Data
  • Sensor Telemetry
  • Industrial IoT Signals
  • Image Workloads
  • High-Volume Video Streams
• Drive implementation of real-time analytics frameworks supporting operational decision-making and intelligent automation.

Operational Excellence & AI Operations

• Ensure production systems achieve high standards for:
  • Availability
  • Reliability
  • Security
  • Scalability
  • Performance
• Lead incident management, operational support, platform governance, and service management processes.
• Establish Service Level Objectives (SLOs), operational metrics, and performance monitoring standards.
• Implement automated observability, alerting, remediation, and reliability engineering practices.
• Drive adoption of MLOps, AIOps, and continuous improvement methodologies.

Business & Stakeholder Partnership

• Collaborate closely with Manufacturing, Assembly & Test, Quality, Planning, Engineering, Operations, and Corporate Functions.
• Translate business priorities into scalable technical solutions and execution plans.
• Drive prioritization, resource planning, technology investments, and delivery governance.
• Communicate strategy, architecture direction, technology roadmaps, and business outcomes to executive leadership.
• Build strong partnerships across global engineering organizations to accelerate innovation and delivery.

Qualifications & Experience Required

• 10+ years of experience delivering enterprise-scale software, cloud, data, analytics, or AI solutions.
• 6+ years of people leadership and engineering management experience.
• Proven experience building and leading high-performing engineering organizations.
• Strong experience designing and deploying cloud-native solutions on:
  • Google Cloud Platform (GCP)
  • Snowflake
  • Azure and/or AWS
• Strong software engineering background with expertise in:
  • Application Development
  • APIs
  • Microservices
  • Distributed Systems
• Experience delivering AI, machine learning, analytics, computer vision, or intelligent automation solutions.
• Strong programming experience in Python and modern software development frameworks.
• Strong expertise in SQL, data platforms, and data engineering concepts.
• Experience working with DevOps, DevSecOps, CI/CD, Infrastructure as Code, and cloud deployment practices.
• Excellent stakeholder management, organizational leadership, and executive communication skills.

Preferred

• Experience with Generative AI, Large Language Models (LLMs), and AI agent frameworks.
• Experience implementing:
  • RAG Architectures
  • Vector Databases
  • Agentic AI Systems
  • Enterprise Knowledge Platforms
  • MLOps and AIOps Frameworks
• Experience with Kubernetes, OpenShift, and cloud-native application platforms.
• Experience delivering:
  • Computer Vision Solutions
  • Image Analytics Platforms
  • Video Intelligence Systems
  • Edge AI Applications
  • Real-Time Data Processing Solutions
• Understanding of Semiconductor Manufacturing and Assembly & Test business domains.
• Experience leveraging AI Coding Assistants, Agentic Development Platforms, and AI-driven engineering tools.
• Exposure to Digital Twins, Physical AI, Autonomous Systems, and Industrial AI platforms.
• Integrates AI-assisted tools and insights into daily work to improve efficiency, quality, or effectiveness, exercising sound judgment and complying with organizational standards and legal requirements.
• Contributes to a culture of continuous improvement by identifying, testing, and sharing AI-enabled enhancements within one’s scope of work.

Education

• Bachelor’s degree in computer science, Computer Engineering, Data Science, Artificial Intelligence, Information Systems, or a related technical discipline.
• Master's Degree preferred.

Role Impact

This role serves as a strategic bridge between business transformation, AI innovation, cloud engineering, software development, and scalable product delivery. The Manager, Full-Stack AI Engineering will play a critical role in accelerating Micron's AI-first vision by delivering intelligent platforms that power manufacturing automation, predictive quality, yield optimization, operational intelligence, and next-generation digital capabilities across the enterprise.