Senior Engineer – Context Intelligence (AI Platform)
Working at Target means helping all families discover the joy of everyday life. We bring that vision to life through our values and culture. Learn more about Target here.

As a Senior Engineer – AI Platforms, Context Intelligence you will be part of a team building the enterprise context and knowledge foundation that enables AI applications and agents to access, retrieve and reason over trusted enterprise information.

You will operate at the intersection of AI engineering, information retrieval, search, knowledge systems, and distributed platform engineering. You will design and build scalable context services that enable semantic and hybrid retrieval, grounding, memory, enterprise knowledge access, and high-quality AI experiences.

A key part of this role is turning complex context and knowledge challenges into reusable platform capabilities. You will build retrieval services, memory systems, knowledge ingestion and indexing pipelines, context orchestration capabilities, and observability that can be adopted consistently across AI applications and agents.

As a Senior Engineer, you serve as a specialist in the engineering team that supports the product. You help develop and gain insight into application architecture. You can distill an abstract architecture into concrete design and influence the implementation. You demonstrate expertise in applying appropriate software engineering patterns to build robust and scalable systems. You are an expert in programming and apply your skills in developing the product. You have the skills to design and implement architecture independently while influencing fellow engineers through software designs, implementation guidance, code reviews, and technical mentorship.

Use your skills, experience, and talents to be part of groundbreaking thinking and visionary goals. As a Sr. Engineer within Context Intelligence, you'll take the lead as you:

• Design, build, and operate scalable retrieval and context services that enable AI applications and agents to access trusted enterprise knowledge.
• Build semantic, hybrid, and enterprise search capabilities using modern information retrieval techniques, embeddings, vector databases, indexing, and search infrastructure.
• Develop retrieval-augmented generation (RAG) and grounding patterns that improve the relevance, quality, and trustworthiness of AI responses.
• Build memory and context-management capabilities for conversational and agentic systems, including short- and long-term context and agent memory patterns.
• Design and implement enterprise knowledge ingestion, content indexing, metadata, synchronization, and connector capabilities that keep knowledge accessible and fresh.
• Build context orchestration services that select, assemble, and optimize the information supplied to AI applications and agents.
• Develop retrieval observability and diagnostics, including grounding quality, retrieval quality, search metrics, freshness, performance, and operational health.
• Apply strong distributed systems and platform engineering principles to build reliable, scalable, secure, and maintainable stateful services and indexing systems.
• Design and operate containerized services on Kubernetes and contribute to resilient, multi-region architectures for enterprise-scale context and knowledge workloads.
• Evaluate emerging retrieval, RAG, GraphRAG, knowledge graph, semantic technology, and enterprise search approaches through research and proof-of-concept activities.
• Lead code reviews, design reviews, testing, debugging, performance optimization, and architecture discussions while maintaining high engineering and operational standards.
• Partner with AI platform, application engineering, product, architecture, and data teams to understand context needs and translate them into reusable platform capabilities.
• Diagnose complex retrieval, indexing, grounding, memory, and production issues, identify systemic causes, and develop solutions that prevent repeat occurrences.
• Share technical patterns and best practices and help engineering teams successfully adopt Context Intelligence platform capabilities.

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

About you:

• 4-year degree or equivalent experience
• 5+ years of software development experience with strong hands-on engineering and programming skills
• Strong experience designing, building, testing, deploying, and operating production-grade applications, platform services, or distributed systems
• Experience with information retrieval, search architecture, RAG, embeddings, vector databases, or related context and knowledge technologies
• Strong proficiency in one or more modern programming languages and experience developing APIs, services, and data-intensive systems
• Experience building or integrating semantic search, hybrid search, retrieval services, indexing pipelines, or enterprise search capabilities
• Understanding of memory systems, context management, grounding, and retrieval patterns for LLM-powered or agentic applications
• Strong understanding of distributed systems, stateful services, scalability, reliability, performance, security, and production operations
• Experience with Kubernetes, containers, CI/CD, observability, testing, debugging, and operational monitoring
• Experience with search infrastructure, distributed indexing, metadata systems, knowledge ingestion, or enterprise data connectors
• Ability to take ambiguous requirements, understand the context needs of AI systems and translate them into concrete technical designs and working software
• Demonstrated ability to evaluate retrieval and system tradeoffs across relevance, latency, freshness, scale, reliability, and cost
• Strong problem-solving skills with the ability to diagnose complex retrieval, indexing, and operational issues and eliminate repeat occurrences
• Experience collaborating with platform, product, application engineering, architecture, and data teams across organizational boundaries
• Ability to communicate complex technical concepts clearly, influence design decisions, mentor engineers, and raise engineering standards within a team
• Maintains technical knowledge within areas of expertise and stays current with evolving retrieval, knowledge, AI, and platform technologies through formal training and self-directed learning

This position will operate as a Hybrid/Flex for Your Day work arrangement based on Target's needs. A Hybrid/Flex for Your Day work arrangement means the team member's core role will need to be performed both onsite at the Target HQ MN location the role is assigned to and virtually, depending upon what your role, team and tasks require for that day. Work duties cannot be performed outside of the country of the primary work location, unless otherwise prescribed by Target. Click here if you are curious to learn more about Minnesota.