Senior AI Engineer - Circlecard

Target • Brooklyn Park, MN

2d • $98,000 - $176,000 • Hybrid

About The Position

As a Senior AI Engineer, you'll combine deep software engineering expertise with modern AI application development to build intelligent, scalable, and highly reliable systems. You'll serve as a technical leader, shaping application architecture, influencing implementation decisions, and establishing engineering best practices. You are equally comfortable designing distributed services and applying Generative AI technologies to solve complex engineering and business problems. You leverage strong software engineering fundamentals to build production-grade AI capabilities that are secure, observable, scalable, and cost-efficient.

The CircleCard Account Services (CAS) team builds and operates the enterprise API platform powering Target's CircleCard ecosystem. Our platform serves as the central gateway for CircleCard account management, new account booking, payments, servicing, compliance processing, and partner integrations. CAS owns a mission-critical platform that enables secure, high-volume financial and non-financial operations across Target. Our services power nearly every aspect of the CircleCard account lifecycle and integrate with numerous enterprise systems.

Current initiatives include:
Building AI-ready platform capabilities and MCP-based tools and services.
Expanding shared platform capabilities, including distributed caching and reusable platform services.
Improving API performance, scalability, resiliency, and observability to support significant traffic growth.
Building AI-powered engineering productivity capabilities.
Automating engineering and operational workflows through intelligent AI agents.
As AI becomes a foundational capability across Target, CAS is investing in intelligent engineering platforms and AI-powered solutions that improve developer productivity, automate operational workflows, and enhance platform reliability.

Requirements

• 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 technologies such as Model Context Protocol (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.
• Expert-level proficiency in Kotlin and Java, with experience building microservices using Micronaut and/or Spring Boot.
• Strong experience designing distributed systems, RESTful APIs, Kafka-based event-driven architectures, PostgreSQL, Redis, Docker, Kubernetes, and modern CI/CD pipelines.
• Experience building highly available, resilient, secure, and observable production services.
• Strong communication, collaboration, and technical leadership skills with experience mentoring engineers, leading design discussions, and driving engineering best practices.
• Passion for applying emerging AI technologies to solve complex engineering and business challenges.

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.
• Design, develop, and maintain highly scalable AI-ready services using Kotlin, Java, and Micronaut.
• Build secure, high-performance RESTful APIs and event-driven services supporting high-volume retail and financial workloads.
• Design distributed systems using Kafka, asynchronous messaging, distributed caching, and cloud-native architecture patterns.
• Design and optimize PostgreSQL databases, including data modeling, indexing, query optimization, and transactional integrity.
• Build resilient, highly available services with strong fault tolerance, scalability, and operational excellence.
• Lead technical design discussions, architecture reviews, code reviews, and implementation planning.
• Improve platform observability using OpenTelemetry, distributed tracing, metrics, logging, and production monitoring.
• Continuously improve application performance, scalability, resiliency, observability, and operational health.
• Partner closely with Product Management, UX, Architecture, Infrastructure, Data, and business stakeholders to deliver customer-focused solutions.

Benefits

• Comprehensive health benefits and programs, which may include medical, vision, dental, life insurance and more
• 401(k)
• Employee discount
• Short term disability
• Long term disability
• Paid sick leave
• Paid national holidays
• Paid vacation

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Associate degree