Machine Learning & AI Engineer

About the Role

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys.

Your Role And Responsibilities

As a Managing Consultant ML/AI Engineer, you own a workstream end-to-end: taking an ambiguous ask, shaping it into a concrete design and plan, and delivering it with minimal guidance. You are responsible for the quality and correctness of everything in your workstream, and you begin to lift the engineers around you.

Key Responsibilities

• Own a workstream: translate an ambiguous business need into a concrete technical approach, choose ML / AI methods and architecture, and defend technical implementation based on performance and trade-offs.
• Design for maintainability: make design decisions that hold up over time; define the tests, validation, and checks that keep your workstream correct as it evolves and guarantee quality.
• Design AI-accelerated workflows: decide where modern AI/LLM tooling fits (and where it doesn't) in your workstream, and set up those workflows for repeatable results.
• Mentor: guide 1–2 junior engineers — review their work, unblock them, raise their bar.
• Engage stakeholders: work directly with business/domain stakeholders to gather requirements and report progress.
• Interpret Data and Communicate Results: Clearly and effectively communicate the results of Machine Learning initiatives to stakeholders, providing actionable insights and recommendations.

This job can be performed from anywhere in the US

Qualifications

Preferred Education: Master's Degree

Required Technical And Professional Expertise

• Strong software engineering: designs maintainable, well-architected solutions; sets and enforces testing/validation for own scope; fluent with version control workflows and reviews.
• End-to-end ML/AI delivery: has independently taken ML/AI work from ambiguous problem to delivered solution, including method & algorithm selection and performance/validation.
• Judgment under ambiguity: frames problems, weighs trade-offs, and makes appropriate decisions.
• Detail & execution: plans and sequences a workstream; manages its risks and dependencies to on-time delivery.
• Modern AI tooling: effective, deliberate use of LLM/agentic tools to accelerate a team's work.
• Emerging leadership: experience mentoring or reviewing others' work.
• Communication & bridging business and technology: engages business/domain stakeholders directly; turns their requirements into a technical approach and explains trade-offs back in terms they understand.