Lead AI Engineer -- Advanced AI (applied Ml, Llms, Agentic Ai, ML Ops)

Target Target · Retail · Brooklyn Park, MN +1

Lead AI Engineer responsible for designing, building, deploying, and maintaining end-to-end AI/ML systems, including LLM-powered applications and agentic architectures. Focuses on creating scalable, reliable, production-grade applications that deliver business value, with an emphasis on strong engineering practices, system design, and collaboration.

What you'd actually do

  1. design, build, deploy, and maintain AI/ML applications that support automation, insight, and action across core business workflows
  2. provide hands-on technical leadership for AI engineering initiatives
  3. contribute to architecture and design decisions, evaluate appropriate models, frameworks, and tools, write maintainable production-quality code, and help establish strong engineering practices across development, testing, deployment, observability, documentation, and ongoing support
  4. partner with senior engineers and engineering leaders to shape technical approaches, identify implementation risks, resolve roadblocks, and support the evolution of reusable AI engineering patterns
  5. help deliver production-grade AI applications that create measurable business value while raising the technical quality and capability of the broader team

Skills

Required

  • Python
  • PyTorch
  • TensorFlow
  • LangChain
  • LlamaIndex
  • Semantic Kernel
  • model APIs
  • prompt orchestration
  • agent development patterns
  • retrieval-augmented generation
  • evaluation frameworks
  • observability tools
  • cloud ML platforms
  • containers
  • orchestration technologies
  • system design
  • application architecture
  • model and framework tradeoffs
  • experimentation
  • evaluation strategy
  • performance optimization
  • production deployment considerations for AI systems
  • scalable, maintainable, and well-tested services, APIs, data pipelines, applications or platforms
  • version control
  • CI/CD
  • code review practices
  • documentation
  • operational monitoring
  • production support
  • communication skills
  • mentoring AI engineers

Nice to have

  • MS in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics or a related technical field

What the JD emphasized

  • 5+ years end to end applied machine learning and of hands-on experience developing AI/ML applications
  • Experience building LLM-powered applications, agentic systems, applied machine learning solutions, data-intensive applications or intelligent automation capabilities
  • Strong understanding of system design, application architecture, model and framework tradeoffs, experimentation, evaluation strategy, performance optimization and production deployment considerations for AI systems

Other signals

  • end-to-end AI/ML systems
  • LLMs
  • agentic architectures
  • production-grade applications
  • scalable, reliable