Principal Engineer - AI /ML Platform

PRINCIPAL ENGINEER – AI PLATFORMS

About Us

Working at Target means helping all families discover the joy of everyday life. We bring that vision to life through our values and culture. Learn more about Target here.

Target's AI Platform organization is building the next generation of enterprise AI capabilities that enable teams to develop, deploy, govern, and operate Machine Learning and Generative AI solutions at scale. Our platform powers AI innovation across the enterprise by providing secure, scalable, and reusable capabilities that accelerate development while maintaining enterprise standards for reliability, governance, and operational excellence.

As a Principal Engineer – ML Operations Platform, you will provide technical leadership in defining the architecture and evolution of our enterprise machine learning platform. You will work across engineering, data science, infrastructure, security, and product organizations to establish scalable patterns for developing, deploying, monitoring, and governing machine learning systems throughout their lifecycle.

This role is ideal for a technology leader who enjoys solving complex platform challenges, influencing engineering strategy, and building capabilities that enable hundreds of engineers and data scientists to deliver AI solutions efficiently and safely.

About the Role:

As a Principal Engineer, you will define the long-term architecture and technical direction for Target's ML Operations Platform. You will establish enterprise-wide standards for machine learning lifecycle management, deployment, governance, observability, and operational excellence while partnering with cross-functional engineering teams to modernize AI platform capabilities. You will influence architecture across multiple organizations, mentor senior engineers, evaluate emerging technologies, and guide strategic platform investments that improve developer productivity and accelerate AI adoption.

Key responsibilities include:

• Define the long-term technical strategy and architecture for the enterprise ML Operations Platform.

• Design scalable, secure, and resilient cloud-native platforms supporting machine learning workloads.

• Establish best practices for model development, deployment, monitoring, and lifecycle management.

• Lead architecture for enterprise machine learning infrastructure supporting batch, streaming, and real-time inference.

• Drive adoption of cloud-native technologies, Kubernetes, and modern platform engineering practices.

• Define standards for model governance, observability, reliability, explainability, and responsible AI.

• Partner with infrastructure, security, and engineering teams to improve platform scalability, performance, and operational efficiency.

• Evaluate emerging technologies and recommend architectural approaches that improve platform capabilities.

• Mentor engineers and influence technical direction across multiple engineering organizations.

Core responsibilities of this job are articulated within this job description. Job duties may change at any time due to business needs.

About You:

• 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