Principal AI/ML Engineer

Job Description

The Principal AI/ML Engineer is a senior individual contributor who sits at the intersection of deep engineering and technical delivery leadership.

This is not a management role, but an amplifier role.

You write serious production code, and you use that credibility to drive the sprint by unblocking engineers, making implementation calls, coordinating across teams, and ensuring that every technical dependency between your squad and the rest of the organization gets resolved fast.

You will report to the Senior Director, AI & Architecture and work closely with the AI Architect and AI Engineers.

You are the engineering face to the engineering teams across the organization.

You are the person who turns AI architecture and strategy into running production systems.

You provide technical leadership and deliver innovative, scalable solutions that leverage artificial intelligence and machine learning to meet complex business needs.

This role balances technical excellence with feasibility, cost, maintainability, and ethical use of AI and data.

You are an exceptional engineer who has grown into the technical leadership of sprint delivery without stepping into architecture ownership or people management.
The successful candidate has a strong track record of delivering production-grade systems, influencing architecture decisions, and partnering effectively with business and technical stakeholders.

This role extends beyond hands-on development to include technical design, mentoring, cross-functional collaboration, and influencing architecture.

ACCOUNTABILITIES:

You'll Own End-to-end AI/ML Solution Delivery:


* Participate in all phases of the AI development lifecycle, including problem framing, data analysis, solution design, model or agent development, evaluation and testing, deployment, monitoring, iterative improvement, support.


* Own the technical execution of sprint deliverables from design through deployment


* Drive daily engineering momentum: run stand-ups from a technical lens, surface blockers early, and resolve them before they become delays


* Make implementation-level decisions confidently and quickly within the established architecture


* Review pull requests with a focus on correctness, performance, security, and long-term maintainability


* Ensure engineering work is aligned to acceptance criteria and Definition of Done including eval thresholds for AI features


* Design, build, and maintain reusable, scalable AI/ML systems, including model pipelines, feature engineering workflows, and inference services.


* Partner with technical and business teams to translate complex business problems into effective AI/ML solutions.


* Provide effort estimation, dependency analysis, and technical risk assessment for initiatives, epics, and complex features.


* Act as a face of the AI Engineering team to the rest of the organization.

Technical Leadership & Governance:


* Provide technical le...