ML/AI Engineer – Senior Managing Consultant

Your role and responsibilities
As a Senior Managing Consultant, you are the hands-on technical driver for a project. You define the technical approach and drive the code and implementation, building the hardest, highest-risk parts of the solution. You guide a small team, keep the work correct, and pull the delivery through by example.

Your primary responsibilities will include:

Turn needs into Machine Learning / AI Solutions: Take high ambiguity from the business and define the problem worth solving. Design and implement systems and models to solve complex business problems, selecting relevant features and algorithms to achieve desired outcomes.
Set the engineering bar by example: establish standards for code quality, testing/validation, version control, and reproducibility — and hold them by writing to that bar yourself. You take on the pieces where getting the details right matters most.
Be the technical point of contact: translate business needs into the build and report progress to stakeholders on the solution.
Guide a small team: lead a team of 2–4. Assign and review work, unblock people, and grow them while building alongside them, not above them.
Own delivery of the solution: own the plan and sequencing, manage the technical risk, and keep the solution on track.
Communicate Results: Clearly articulate the results of Machine Learning initiatives, providing actionable insights and recommendations to drive business outcomes.
Drive Informed Decision-Making: Collaborate with stakeholders to integrate Machine Learning insights into business decision-making processes, driving informed strategic choices.
Leverage Agentic AI & Modern LLMs: Claude, Copilot, LLM agents, prompt engineering, intelligent pipelines
Design Scalable ML Platforms: MLOps infrastructure, enterprise integration
Mentor & Collaborate: Cross-functional partnerships, team building

This job can be performed from anywhere in the US.

Required education
Bachelor's Degree
Preferred education
Master's Degree

Required technical and professional expertise
Deep software-engineering discipline: sets testing/validation and reproducibility standards for a team and lives them in the code; known for rigor and correctness.
Technical leadership of a small team: has guided a small team's technical work, owning the approach and reviewing others, while remaining a primary builder.
Delivery ownership: credible at planning and sequencing a solution and managing its technical risk to delivery.
Advanced ML/AI: broad command of ML/AI methods and modern AI/LLM/agentic approaches; sound judgment on where each applies.
Communication & bridging business and technology: the primary bridge between business and engineering for the solution. Comfortable being the technical point of contact, translating business needs into the build and technical reality back into decisions stakeholders can act on.