Lead Architect: Enterprise ML Platform

Target; Brooklyn Park (MN); Hybrid; USD 168,000 - 356,000; Full time

Benefits offered by this job

Remote or Hybrid work options

Benefits eligibility

Job summary

Target seeks a Principal Engineer – ML Operations Platform to define and lead the architecture for the enterprise ML platform. You will partner with engineering, data science, and security to scale, govern, and monitor machine learning systems.

You will mentor engineers, evaluate new technologies, and drive modernization across multiple teams while championing scalable, reliable AI solutions at scale.

Qualifications

• MS in Computer Science, Engineering, Mathematics, or related field.
• Extensive experience delivering large-scale cloud-native platforms.
• Deep experience with enterprise ML platforms and MLOps capabilities.
• Strong knowledge of ML lifecycle management, deployment strategies, and observability.
• Excellent communication and leadership to influence across teams.

Responsibilities

• Define long-term architecture for Target's ML Operations Platform.
• Design scalable, secure cloud-native platforms for ML workloads.
• Establish standards for model governance, deployment, and monitoring.
• Lead enterprise ML infrastructure across batch, streaming, and real-time inference.
• Drive adoption of cloud-native technologies and platform engineering practices.
• Mentor engineers and guide multiple teams on architecture and modernization.
• Evaluate emerging tech and steer investments to improve platform capabilities.
• Collaborate with security, infrastructure, and data science teams.

Skills

Leadership

Architectural thinking

Communication

CI/CD

Observability

MLOps

Cloud platforms

Mentorship

Education

MS in Computer Science

Tools

Vertex AI

Kubeflow

MLflow

Terraform

GitOps

Kubernetes

Service Mesh

Job description

Target seeks a Principal Engineer – ML Operations Platform to define and lead the architecture for the enterprise ML platform. You will partner with engineering, data science, and security to scale, govern, and monitor machine learning systems.

You will mentor engineers, evaluate new technologies, and drive modernization across multiple teams while championing scalable, reliable AI solutions at scale.