Principal Engineer - AI /ml Platform

Target Target · Retail · Brooklyn Park, MN +1

Target is seeking a Principal Engineer to lead the architecture and evolution of their enterprise machine learning platform. This role involves defining technical strategy, establishing standards for ML lifecycle management, deployment, governance, and observability, and partnering with cross-functional teams to build scalable, secure, and resilient cloud-native platforms for ML workloads. The ideal candidate will have deep experience in MLOps, cloud-native technologies, Kubernetes, and supporting large-scale inference and foundation model workloads.

What you'd actually do

  1. Define the long-term technical strategy and architecture for the enterprise ML Operations Platform.
  2. Design scalable, secure, and resilient cloud-native platforms supporting machine learning workloads.
  3. Establish best practices for model development, deployment, monitoring, and lifecycle management.
  4. Lead architecture for enterprise machine learning infrastructure supporting batch, streaming, and real-time inference.
  5. Drive adoption of cloud-native technologies, Kubernetes, and modern platform engineering practices.

Skills

Required

  • MS in Computer Science, Engineering, Mathematics, or related technical field with relevant software engineering experience
  • Extensive experience designing and delivering large-scale cloud-native platforms or distributed systems
  • Deep experience building and operating enterprise machine learning platforms and MLOps capabilities
  • Strong understanding of machine learning lifecycle management, deployment strategies, observability and production operations
  • Demonstrated experience with machine learning platforms and tooling such as Vertex AI, Kubeflow, MLflow, and/or equivalent technologies
  • Experience building developer platforms or internal platform products
  • Experience with distributed training, GPU infrastructure, and large-scale inference platforms
  • Experience with feature management, model governance, and responsible AI practices.
  • Familiarity with Generative AI platforms and infrastructure supporting foundation model workloads
  • Experience with Terraform, GitOps, service mesh technologies, and platform automation
  • Experience mentoring senior engineers and leading enterprise-scale modernization initiatives
  • Expertise designing Kubernetes-based platforms supporting AI and machine learning workloads
  • Strong understanding of software engineering best practices including CI/CD, infrastructure as code, observability, testing, and automation
  • Experience defining technical strategy, architectural standards and engineering best practices across multiple teams
  • Excellent communication and influencing skills with the ability to communicate complex technical concepts to engineering and business leaders

What the JD emphasized

  • enterprise machine learning platforms
  • MLOps capabilities
  • model development, deployment, monitoring, and lifecycle management
  • scalable, secure, and resilient cloud-native platforms supporting machine learning workloads
  • Generative AI platforms and infrastructure supporting foundation model workloads

Other signals

  • enterprise machine learning platforms
  • MLOps capabilities
  • model development, deployment, monitoring, and lifecycle management
  • scalable, secure, and resilient cloud-native platforms supporting machine learning workloads
  • Generative AI platforms and infrastructure supporting foundation model workloads