AI/ML Platform Engineer

About Us

Mercedes-Benz USA is responsible for the sales, marketing and service of all Mercedes-Benz and Maybach products in the United States. In our people, you will find tremendous commitment to our corporate values\: 'PRIDE = Passion, Respect, Integrity, Discipline, and Execution'. Our products and employees reflect this dedication. We are looking for diverse top-notch individuals to join the Mercedes-Benz Team and uphold these hallmarks.

Job Overview

The AI/ML Platform Engineer is responsible for the foundational platform capabilities that power AI and machine learning delivery across Mercedes-Benz USA. This role designs, builds, and operates shared AI/ML infrastructure, deployment pipelines, model lifecycle tooling, and reusable engineering services that enable data scientists, AI engineers, and business teams to develop and scale AI solutions efficiently.

As the platform foundation of the Applied AI Engineering & Operations team, this role supports classical machine learning, generative AI, agent-based solutions, and enterprise-scale analytics workloads. The ideal candidate combines strong platform engineering expertise, cloud experience, MLOps knowledge, and production operations experience with a passion for building reliable and scalable engineering foundations.

Responsibilities

AI/ML Platform Engineering & Delivery (60%)

• Design, build, and operate enterprise AI/ML platform capabilities and shared engineering services.
• Develop and maintain model deployment pipelines, model registry capabilities, experiment tracking, and foundational MLOps tooling.
• Create reusable platform components, templates, automation frameworks, and deployment standards that accelerate AI delivery.
• Provide the platform foundations supporting machine learning, generative AI, agent-based solutions, and AI productization initiatives.
• Design and manage multi-tenancy patterns, resource isolation strategies, and workload governance across teams and business domains.
• Ensure platform reliability, scalability, security, observability, and cost optimization across AI workloads.
• Drive operational excellence through monitoring, incident response, resiliency improvements, and continuous platform enhancements.
  

Platform Architecture & Engineering Standards (20%)

• Define and evolve platform architecture, deployment patterns, and engineering standards for AI/ML delivery.
• Evaluate emerging platform technologies and engineering approaches that improve scalability, performance, and developer productivity.
• Partner with architecture, infrastructure, security, and engineering teams to ensure alignment with enterprise standards.
• Provide technical leadership for platform investments, architecture decisions, and modernization initiatives.
  

Operational Excellence & Reliability (10%)

• Establish best practices for monitoring, logging, performance management, platform support, and operational readiness.
• Develop engineering standards, documentation, automation, and operational runbooks.
• Promote continuous improvement of platform reliability, supportability, and operational maturity.
  

Collaboration & Technical Leadership (10%)

• Collaborate with data scientists, AI engineers, architects, infrastructure teams, and business stakeholders.
• Provide technical mentorship and guidance across the AI Engineering organization.
• Support knowledge sharing, cross-training, and engineering excellence initiatives.