Sr AI Engineer - Advanced AI (ml Ops, Llms, Agentic Workflows)

Target Target · Retail · Brooklyn Park, MN +1

Sr AI Engineer role focused on building, deploying, and maintaining end-to-end AI/ML systems, including LLM-powered and agentic architectures, for enterprise business value. Requires strong software engineering skills, experience with AI/ML frameworks, and collaboration with cross-functional teams.

What you'd actually do

  1. help build, deploy, and maintain AI/ML applications that support automation, insight, and action across core business workflows
  2. work closely with Data Scientists, engineers, product partners, platform teams, security teams, and business stakeholders to turn well-defined and moderately ambiguous business problems into practical technical solutions
  3. contribute hands-on to the development of production-grade AI applications
  4. write maintainable, well-tested code, support model and framework integration, build APIs and services, develop data and application workflows, and contribute to deployment, monitoring, documentation, and production support
  5. help ensure AI applications are secure, reliable, maintainable, and aligned to Target’s enterprise standards for infrastructure, platform architecture, data handling, and operational readiness

Skills

Required

  • Python
  • PyTorch
  • TensorFlow
  • LangChain
  • LlamaIndex
  • Semantic Kernel
  • model APIs
  • prompt orchestration
  • retrieval-augmented generation
  • evaluation approaches
  • observability tools
  • cloud platforms
  • containers
  • orchestration technologies
  • system design
  • application architecture
  • performance optimization
  • production deployment
  • maintainable and well-tested services
  • APIs
  • data pipelines
  • applications
  • platforms
  • version control
  • CI/CD
  • code review practices
  • documentation
  • operational monitoring
  • production support

Nice to have

  • agentic systems at scale

What the JD emphasized

  • Experience developing AI/ML applications including LLM-powered applications, applied machine learning solutions, data-intensive applications, intelligent automation capabilities and agentic systems at scale
  • Strong proficiency with Python and experience with AI/ML or deep learning frameworks such as PyTorch, TensorFlow, LangChain, LlamaIndex, Semantic Kernel or similar tools
  • Experience working with model APIs, prompt orchestration, retrieval-augmented generation, evaluation approaches, observability tools, cloud platforms, containers or orchestration technologies
  • Understanding of system design, application architecture, model and framework tradeoffs, experimentation, evaluation, performance optimization and production deployment considerations for AI systems
  • Experience building maintainable and well-tested services, APIs, data pipelines, applications or platforms

Other signals

  • build, deploy, and maintain AI/ML applications
  • agentic architectures
  • production-grade AI applications
  • LLM-powered applications
  • agentic systems at scale