Senior AI Engineer - Circlecard

Senior AI Engineer (Backend Platform) – Circle Card Account Services (CAS)

Join Target's Circle Card Account Services team to build intelligent, scalable, and highly reliable systems powered by AI. You will serve as a technical leader shaping architecture, influencing implementation decisions, and establishing engineering best practices.

• Leverage AI-assisted software development tools throughout the lifecycle, including design, implementation, testing, debugging, documentation, and code reviews.
• Collaborate with AI coding assistants and enterprise AI platforms to improve productivity while maintaining high standards for code quality, security, architecture, and maintainability.
• Validate AI-generated code and recommendations through testing, peer reviews, and established software engineering practices.
• Design, build, and deploy production AI-powered applications using approved AI platforms, frameworks, and large language models to solve engineering and business problems.
• Build 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 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 delivering measurable business value.
• Partner across engineering teams to integrate AI capabilities into products, platforms, and 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 Open Telemetry, 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.

• 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.
• Experience building autonomous or multi‑agent workflows with frameworks such as Model Context Protocol, Lang Graph, Lang Chain, CrewAI, Google ADK, Semantic Kernel, or equivalent.
• Strong understanding of AI application architecture, including prompt engineering, context engineering, retrieval‑augmented generation, 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…