AI/ML Platform Engineer

Build and operate the shared infrastructure, deployment pipelines, model lifecycle tools, and engineering services that support AI and machine learning delivery. This role provides scalable platform foundations for classical machine learning, generative AI, agent-based solutions, and enterprise analytics workloads. You will work within the Applied AI Engineering and Operations organization alongside data scientists, AI engineers, architects, infrastructure teams, and business stakeholders., * Design, build, and operate enterprise AI/ML platform capabilities and shared engineering services.

• Develop model deployment pipelines, model registry capabilities, experiment tracking, and MLOps tooling.
• Create reusable platform components, templates, automation frameworks, and deployment standards.
• Design 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.
• Define platform architecture, deployment patterns, and engineering standards for AI/ML delivery.
• Establish monitoring, logging, performance management, platform support, and operational readiness practices.
• Provide technical guidance and mentorship across the AI Engineering organization.

• 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 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, Docker, Kubernetes, Infrastructure as Code, and cloud-native deployment architectures.

Apply if you have the platform engineering and MLOps experience to build reliable, scalable foundations for enterprise AI delivery. This hybrid role also requires regular collaboration with teams across multiple time zones and some travel for team, partner, and business engagements.