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Machine Learning & AI Engineer Managing Consultant
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What they do
A Machine Learning Engineer specializes in designing, building, and deploying machine learning models. They utilize statistical and mathematical techniques, parallelizing processing, hyperparameter tuning, and other optimization methodologies to improve model performance. Responsibilities also include collecting and preprocessing large datasets, conducting exploratory data analysis, working closely with data engineers to understand data requirements, and engineer input variables for machine learning models.
$124,597 / year median in the U.S.
Job Description
- Introduction
- A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide.
- 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.
Your primary responsibilities will include:
- 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
- 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.
- Preferred technical and professional experience
Benefits
- Dental Insurance