AI Platform Engineer (AI COE)

Lineage Logistics•Detroit, MI

1d•Remote

About The Position

The AI Platform Engineer is accountable for designing, building, and operating the core AI platform that is at the center of Lineage’s AI strategy. With 400+ facilities in two dozen countries, the opportunity to safely and reliably put trusted AI capabilities into the hands of Lineage Logistics’ teams has never been greater. The platform this role builds is the foundation that our AI solutions depend on, from model serving and evaluation pipelines to developer environments and deployment automation. At Lineage, the aim is not to build the largest technology team; instead, we are creating a small, highly skilled group that focuses on having the right mix of talents, skills, and the capacity to learn in areas that define our core differentiation. We leverage cloud services, best-of-breed libraries and components, and external teams to augment our capabilities with sufficient capacity. This means that in addition to being a firsthand technologist, you also need to be able to review and collaborate with internal and external partners to deliver, and to build a platform that enables Forward Deployed Engineers and other colleagues to ship use cases rapidly and safely. As a company, Lineage builds state-of-the-art systems to make our employees productive, to meet and exceed our customers’ expectations, to assist in our growth, and to create a technology platform that enables Lineage to achieve its vision of transforming the food supply chain. This is a foundational role in that journey, and it will require someone who has a true passion for building secure, scalable platforms and for the responsible application of AI at enterprise scale.

Requirements

University degree in computer science, software engineering, or a related field and/or > 10 years of relevant work experience.
5+ years of experience in platform engineering, infrastructure engineering, MLOps, or a related discipline.
Hands-on experience building and operating ML/AI serving infrastructure (e.g., model registries, inference endpoints, evaluation pipelines).
Strong proficiency with cloud platforms (AWS, Azure, or GCP) and container orchestration (Kubernetes, Docker).
Experience with infrastructure-as-code (Terraform, Pulumi) and CI/CD tooling.
Solid understanding of security and compliance requirements in regulated enterprise environments.
Demonstrate excellent verbal and written communication skills, including the ability to translate platform capabilities into terms business and governance stakeholders understand.
Possess a willingness to be coached and proactively seek feedback.
Manage multiple assignments / projects simultaneously.
Properly set priority and order for work.
Work in a fast-paced, agile environment.
Ask for help and provide it to others.
Understand your skills and your talents and apply them appropriately.
Learn and apply modern technologies and business concepts.
Mentor and provide guidance, especially to other technical resources.

Nice To Haves

Experience standing up a new platform or developer platform from scratch (greenfield builds) is strongly preferred.
Familiarity with LLM deployment patterns, agent frameworks, and retrieval-augmented generation (RAG) architectures is a plus.
Experience with model evaluation tooling and red-teaming methodologies is a plus.
Previous exposure to supply chain technologies including WMS, TMS, YMS, and related systems is a plus.

Responsibilities

Design, deploy, and operate Lineage’s core AI platform, including model hosting, orchestration, and evaluation (evals) infrastructure.
Build and maintain CI/CD pipelines, developer tooling, and sandbox environments that enable Forward Deployed Engineers and Skills/Agent Engineers to ship use cases rapidly.
Implement guardrails, monitoring, and observability to ensure platform safety, security, and compliance with enterprise IT, Cyber, Legal, and Risk requirements.
Collaborate with IT Security, Legal, and Risk stakeholders to embed governance into the platform from day one.
Define and enforce platform standards, including APIs, SDKs, and model packaging conventions, so that integrations remain consistent and maintainable as the platform scales.
Partner with the Site Reliability Engineer s to establish SLOs, incident response, and capacity planning as the platform scales.
Contribute to the evaluation framework by building tooling for automated model testing, regression detection, and performance benchmarking.
Consistently deliver high-quality software and processes in usable increments.
Grow and maintain a broad and deep knowledge of supply chain operations and the ways AI can be applied to them.