Principal AI Engineer - Advanced AI (Machine Learning, Python, Deep Learning)

Target•Brooklyn Park, MN

22h•Hybrid

About The Position

As a Principal AI Engineer - Advanced AI, you'll join Target's Advanced AI team, a centralized group focused on building and owning capabilities that use advanced AI technologies to drive automation, insight, and action across core business workflows. In this role, you'll provide deep technical leadership across enterprise AI initiatives by partnering with engineering, product, and business teams to shape scalable solution patterns, accelerate development, and deliver high-impact AI applications that optimize business workflows and create measurable value. You will help design, build, deploy, and scale end-to-end AI solutions, including architecting agentic and LLM-powered systems, evaluating and selecting the right models and frameworks, and establishing strong observability, evaluation, and engineering practices to ensure solutions are production-ready, maintainable, and well-documented. Given the pace of change in this space, you will also stay current on emerging technologies, recommend tooling and architectural approaches, publish learnings for the team and broader organization, and influence technical direction and engineering standards across Advanced AI initiatives. We utilize Agile principles, follow best-practice software design, participate in code reviews, and create maintainable, well-tested codebases with relevant documentation. Core responsibilities of this job are articulated within this job description. Job duties may change at any time due to business needs.

Requirements

PhD or MS in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics or a related technical field preferred
Deep experience designing and delivering real-world AI and machine learning solutions including LLM-based, agentic, or other advanced AI systems in production environments
7 plus years of demonstrated hands-on experience in software engineering and applied AI/ML development - including Python and modern ML / deep learning frameworks such as PyTorch, TensorFlow or similar tools
Extensive experience with AI engineering tooling and platforms such as agent development frameworks, model APIs, evaluation and observability frameworks, cloud ML platforms, containers, and orchestration technologies
Strong understanding of system design, model and architecture tradeoffs, experimentation, evaluation strategy, performance optimization, and production deployment considerations for AI systems
Experience building scalable, maintainable, and well-tested services or platforms including version control, CI/CD, code review practices, and operational monitoring
Ability to translate ambiguous business problems into clear technical approaches and create strong technical documentation, narratives, and recommendations for a range of audiences
Excellent communication and influencing skills, with the ability to explain complex technical concepts to both technical and non-technical partners and leaders
Self-driven and results-oriented, with strong ownership, sound judgment, and the ability to move quickly while maintaining high technical standards in a fast-evolving domain
Collaborative team player with experience working effectively across functions, organizations, and geographies, and with a demonstrated commitment to continuous learning and knowledge sharing

Responsibilities

Provide deep technical leadership across enterprise AI initiatives by partnering with engineering, product, and business teams to shape scalable solution patterns, accelerate development, and deliver high-impact AI applications that optimize business workflows and create measurable value.
Design, build, deploy, and scale end-to-end AI solutions, including architecting agentic and LLM-powered systems.
Evaluate and select the right models and frameworks.
Establish strong observability, evaluation, and engineering practices to ensure solutions are production-ready, maintainable, and well-documented.
Stay current on emerging technologies, recommend tooling and architectural approaches.
Publish learnings for the team and broader organization.
Influence technical direction and engineering standards across Advanced AI initiatives.
Utilize Agile principles, follow best-practice software design, participate in code reviews, and create maintainable, well-tested codebases with relevant documentation.