Software Engineering Manager-AI/ML

KLA•Ann Arbor, MI

1d•$114,100 - $194,000•Onsite

About The Position

The AI Manager leads a team of engineers delivering AI enabled products and platforms that drive measurable business outcomes. This role combines people leadership, technical oversight, and delivery accountability, ensuring AI solutions are built responsibly, operate reliably in production, and align with enterprise priorities. The manager partners closely with product, data, platform, security, and business stakeholders to translate opportunities into scalable AI solutions.

Requirements

Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience).
10+ years of experience in software engineering, ML/AI engineering, or applied data roles.
4+ years of people management experience leading technical teams.
Strong understanding of AI/ML systems and how they are built, deployed, and operated in production.
Hands‑on background with modern engineering practices (APIs, CI/CD, testing, observability).
Ability to communicate effectively with both technical and non‑technical stakeholders.

Nice To Haves

Experience managing teams building LLM‑based applications or GenAI platforms.
Familiarity with cloud platforms (e.g., Azure, AWS, or GCP) and modern data stacks.
Exposure to MLOps practices, model monitoring, and AI governance frameworks.
Experience delivering AI solutions in enterprise domains such as manufacturing, supply chain, finance, or customer operations.

Responsibilities

Manage and grow a team of AI engineers, ML engineers, and/or software engineers working on AI initiatives.
Set clear goals, expectations, and success metrics; conduct regular 1:1s, performance reviews, and career development planning.
Coach team members on technical excellence, delivery discipline, and responsible AI practices.
Own delivery of AI and GenAI initiatives from concept through production, ensuring scope, timelines, and quality standards are met.
Guide teams in building production‑grade AI systems, including data pipelines, model training, LLM applications, deployment, and monitoring.
Balance experimentation with operational rigor—ensuring solutions are scalable, secure, and supportable.
Provide architectural and design guidance for: ML pipelines and model lifecycle management LLM applications (e.g., RAG, agents, copilots, tool/function calling) Integration of AI services into enterprise applications
Review designs and key technical decisions; ensure alignment with platform standards and best practices.
Partner with data, cloud, and platform teams to ensure AI solutions align with enterprise architecture.
Collaborate with security, legal, and governance teams to ensure responsible AI, privacy, and compliance requirements are met.
Communicate status, risks, and outcomes clearly to leadership and stakeholders.
Ensure AI systems meet standards for reliability, monitoring, cost management, and incident response.
Establish guardrails for responsible AI: data privacy, model risk, transparency, bias awareness, and auditability.
Drive continuous improvement through retrospectives, metrics, and adoption of best practices.