Lead AI Engineer- Advanced AI

Target’s Advanced AI team builds end-to-end AI/ML systems that create meaningful business value across the enterprise. These systems may be powered by LLMs, classical machine learning, or deep learning models, and are designed as scalable, reliable, production-grade applications, including agentic architectures where they add clear value.

As a Lead AI Engineer for Advanced AI, you will help design, build, deploy, and maintain AI/ML applications that support automation, insight, and action across core business workflows. You will work closely with Data Scientists, engineers, product partners, platform teams, security teams, and business stakeholders to turn ambiguous business problems into practical, scalable technical solutions.

In this role, you will provide hands-on technical leadership for AI engineering initiatives. You will 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. You will help ensure AI applications are secure, reliable, maintainable, and aligned to Target’s enterprise standards for infrastructure, platform architecture, data handling, and operational readiness.

You will also 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. This role requires curiosity and continuous learning, including staying current with developments in AI, machine learning, LLMs, agentic systems, and modern software engineering practices. A successful Lead AI Engineer will help deliver production-grade AI applications that create measurable business value while raising the technical quality and capability of the broader team.

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

4-year degree in Quantitative disciplines (Science, Tech, Engineering, Mathematics) or equivalent industry experience required; MS in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics or a related technical field preferred.

6+ years of hands-on software engineering experience, including experience building and operating production systems.

Strong experience developing AI/ML applications, including LLM-powered applications, agentic systems, applied machine learning solutions, data-intensive applications, or intelligent automation capabilities.

Strong proficiency with Python and experience with modern AI/ML or deep learning frameworks such as PyTorch, TensorFlow, LangChain, LlamaIndex, Semantic Kernel, or similar tools.

Experience working with model APIs, prompt orchestration, agent development patterns, retrieval-augmented generation, evaluation frameworks, observability tools, cloud ML platforms, containers, or orchestration technologies.

Strong understanding of system design, application architecture, model and framework tradeoffs, experimentation, evaluation strategy, performance optimization, and production deployment considerations for AI systems.

Experience building scalable, maintainable, and well-tested services, APIs, data pipelines, applications, or platforms.

Experience with version control, CI/CD, code review practices, documentation, operational monitoring, and production support.

Ability to translate ambiguous business problems into clear technical approaches and collaborate with cross-functional partners to deliver practical solutions.

Strong communication skills, with the ability to explain technical concepts clearly to engineers, Data Scientists, product partners, business stakeholders, and leaders.

Ability to mentor engineers, contribute to technical direction, and raise the quality of engineering practices within a team.

Self-driven and results-oriented, with strong ownership, sound judgment, and the ability to move quickly while maintaining high technical standards.

Collaborative team player with a commitment to continuous learning, knowledge sharing, and building reliable AI systems that create business value.

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