Sr AI Engineer

As a Sr AI Engineer in the Data Sciences team , you will help build, deploy, and maintain AI/ML applications that support automation, insight s 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 well-defined and moderately ambiguous business problems into practical technical solutions.

In this role, you will contribute hands-on to the development of production-grade AI /ML/Engineering systems . You will 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. 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, Lead, and Principal Engineers to evaluate technical options, identify implementation risks, resolve engineering roadblocks, and improve reusable AI engineering patterns. This role requires curiosity and continuous learning, including staying current with developments in AI, machine learning, agentic systems, and modern software engineering practices. A successful Senior AI Engineer will help deliver high-quality ML/ AI applications and serving systems that create measurable business value while contributing to the technical strength of the 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 a quantitative discipline (Science, Technology, Engineering, Mathematics) or equivalent experience. • 5+ years of hands-on software engineering experience, including building or supporting production DS/ML systems. • Experience building and scaling production ML/AI applications, including applied ML solutions, intelligent automation, and agentic systems. • Strong proficiency in Python, with hands-on experience productionizing models and building APIs, services, data/model pipelines, batch or online inference, and retrieval/vector-based systems. • Experience with MLOps and production AI practices, including model evaluation, observability, CI/CD, containers, cloud deployment, reliability, and performance optimization. Experience with GCP is preferred. • Strong understanding of system design and AI application architecture, including model/framework trade-offs, experimentation, evaluation, scalability, latency, and production deployment considerations. • Experience building maintainable, well-tested production systems using sound software engineering practices, including version control, code reviews, documentation, monitoring, and production support. • Ability to translate business and technical requirements into pragmatic engineering solutions, work independently on defined workstreams, and collaborate effectively with engineers, applied data scientists, product partners, and business stakeholders. • Strong ownership, communication, and engineering judgment, with a collaborative mindset and a focus on delivering reliable AI systems that create measurable business value. • Experience operating ML/AI systems at scale, including performance optimization, monitoring, experimentation, and production reliability. Exposure to LLM evaluation, retrieval quality, and agent observability is a plus.

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