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
Qualifikationen Technical Skills & Tools
Required
- Strong proficiency in Python, SQL, PySpark, and distributed data processing frameworks.
- Experience with Azure Databricks, including Unity Catalog, Delta Lake, MLflow, Feature Store, and Model Serving.
- Experience with Azure, AWS, or comparable cloud platforms supporting enterprise AI and machine learning workloads.
- Experience with model deployment pipelines, model registry management, experiment tracking, monitoring, and lifecycle management.
- Experience with CI/CD, workflow orchestration, and production AI platform operations.
- Experience with model serving, inference optimization, and scalable AI infrastructure.
- Experience with Docker, Kubernetes, Infrastructure as Code, and cloud-native deployment architectures.
- Experience supporting GPU-enabled workloads, distributed compute environments, and enterprise-scale platform operations.
- Experience with event-driven architectures, streaming technologies, and platform integration patterns.
- Experience with observability platforms, performance optimization, reliability engineering, and cloud cost management.
- Strong software engineering, automation, and production support practices
Preferred Skillset
- Experience with Azure OpenAI, AWS Bedrock, or equivalent enterprise AI platforms.
- Experience with vector databases and retrieval technologies.
- Experience supporting generative AI and agent-based solutions at scale.
- Familiarity with Responsible AI, AI governance, security, and risk management frameworks
Qualifications
Required
- Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or a related technical field.
- 8+ years of experience in software engineering, machine learning engineering, AI platform engineering, or related disciplines.
- Demonstrated experience designing, building, and operating enterprise AI/ML platforms.
- Strong understanding of cloud-native architectures, MLOps, deployment automation, monitoring, and production operations.
- Experience building reusable engineering frameworks, platform services, or shared infrastructure capabilities.
- Strong communication, collaboration, and stakeholder management skills
Preferred Experience
- Master's degree in Computer Science, Engineering, AI/ML, or related field.
- Experience delivering enterprise-scale AI/ML platforms supporting multiple business domains.
- Experience operating in regulated or compliance-sensitive environments.
- Experience scaling platform engineering capabilities supporting machine learning, generative AI, and agent-based systems
Additional Information
- Position requires regular collaboration with business, technology, and external partner teams across multiple time zones.
- Some travel required for team, partner, and business engagements.
- This role is part of MBUSA's Data Insights & AI organization and contributes to the company's long-term AI strategy and operating model.
