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

Target Target · Retail · NCD-0375 Brooklyn Park, MN

Principal AI Engineer focused on building and scaling advanced AI capabilities, including agentic and LLM-powered systems, for enterprise-wide automation and optimization. The role involves deep technical leadership, end-to-end solution design, deployment, and establishing robust engineering practices for production AI applications.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. utilize Agile principles, follow best-practice software design, participate in code reviews, and create maintainable, well-tested codebases with relevant documentation.

Skills

Required

  • 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
  • Python
  • modern ML / deep learning frameworks such as PyTorch, TensorFlow or similar tools
  • AI engineering tooling and platforms such as agent development frameworks, model APIs, evaluation and observability frameworks, cloud ML platforms, containers, and orchestration technologies
  • system design
  • model and architecture tradeoffs
  • experimentation
  • evaluation strategy
  • performance optimization
  • production deployment considerations for AI systems
  • building scalable, maintainable, and well-tested services or platforms
  • version control
  • CI/CD
  • code review practices
  • operational monitoring
  • translate ambiguous business problems into clear technical approaches
  • create strong technical documentation, narratives, and recommendations
  • Excellent communication and influencing skills
  • explain complex technical concepts to both technical and non-technical partners and leaders
  • Self-driven and results-oriented
  • strong ownership
  • sound judgment
  • ability to move quickly while maintaining high technical standards
  • Collaborative team player
  • working effectively across functions, organizations, and geographies
  • demonstrated commitment to continuous learning and knowledge sharing

What the JD emphasized

  • Deep experience designing and delivering real-world AI and machine learning solutions including LLM-based, agentic, or other advanced AI systems in production environments
  • 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

Other signals

  • building and owning capabilities that use advanced AI technologies
  • architecting agentic and LLM-powered systems
  • evaluating and selecting the right models and frameworks
  • establishing strong observability, evaluation, and engineering practices
  • production-ready, maintainable, and well-documented solutions