Senior AI Engineer, Backend Platform — Hybrid

Target

Brooklyn Park (MN)

Hybrid

USD 98,000 - 176,000

Full time

14 days+

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Job summary

Target invites applications for a Senior AI Engineer (Backend Platform) within CircleCard Account Services. You will lead AI-powered software development, build production-grade autonomous systems, and drive AI strategy across enterprise services.

The role emphasizes collaboration with enterprise AI platforms, architecture governance, and scalable design. You will work in a hybrid setup at Target's Minnesota location and remote as needed, with a focus on delivering reliable, secure, and

Qualifications

Bachelor's degree or equivalent practical experience.
5+ years of software engineering experience building enterprise-scale distributed applications and services.
Hands-on experience designing and deploying production AI applications, AI-powered tools, or agentic systems using modern Large Language Models (LLMs).
Experience building autonomous or multi-agent workflows using MCP, LangGraph, LangChain, CrewAI, Google ADK, Semantic Kernel, or equivalent orchestration frameworks.
Strong understanding of AI application architecture, including Prompt Engineering, Context Engineering, Retrieval-Augmented Generation (RAG), tool calling, memory management, orchestration patterns, and AI evaluation.
Experience optimizing AI applications for quality, latency, token efficiency, and inference cost while implementing responsible AI, governance, and observability practices.
Proficiency in Python for AI application development, automation, or AI tooling.

Responsibilities

Leverage AI-assisted software development tools throughout the software development lifecycle, including solution design, implementation, testing, debugging, documentation, and code reviews.
Collaborate effectively with AI coding assistants and enterprise AI platforms to improve engineering productivity while maintaining high standards for code quality, security, architecture, and maintainability.
Apply sound engineering judgment to validate AI-generated code and technical recommendations through testing, peer reviews, and established software engineering practices.
Design and build production AI-powered applications using Target-approved AI platforms, frameworks, and Large Language Models (LLMs) to solve engineering and business problems.
Design, build, and deploy production-grade autonomous and agentic AI systems capable of reasoning, planning, tool orchestration, memory management, and multi-step workflow execution.
Develop domain-specific AI agents and multi-agent workflows using modern orchestration frameworks to automate engineering processes and enhance retail and enterprise experiences.
Design and implement prompt engineering and Context Engineering strategies to improve application quality, reliability, and relevance.
Build Retrieval-Augmented Generation (RAG) solutions leveraging enterprise knowledge sources, vector search technologies, and semantic retrieval.
Optimize AI applications for response quality, latency, throughput, token utilization, and inference cost.
Implement AI evaluation frameworks and apply responsible AI, security, privacy, governance, and observability best practices.
Build reusable AI components, frameworks, libraries, workflows, and engineering accelerators that improve developer productivity.
Evaluate emerging AI capabilities, lead proof-of-concept initiatives, and recommend adoption strategies that deliver measurable business value.
Partner across engineering teams to integrate AI capabilities into products, platforms, and software development workflows.