Senior AI Platforms Engineer, Context Intelligence (Hybrid)

Target

Brooklyn Park (MN)

Hybrid

USD 98,000 - 176,000

Full time

14 hours ago

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Benefits offered by this job

Health benefits

401(k)

Paid time off

Job summary

Target seeks a Senior Engineer – Context Intelligence (AI Platform) in Minnesota to build an enterprise context and knowledge foundation enabling AI applications and agents to access trusted information. You will design scalable retrieval services, memory systems, and indexing pipelines, with strong emphasis on distributed systems and platform reliability.

You will lead cross-functional collaboration with AI platform, application engineering, product, architecture, and data teams to translate

Qualifications

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 translate ambiguous requirements 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 for complex retrieval, indexing, and operational issues.
Experience collaborating across platform, product, application engineering, architecture, and data teams.

Responsibilities

Design, build, and operate scalable retrieval and context services for AI applications.
Build semantic, hybrid, and enterprise search capabilities using information retrieval techniques, embeddings, vector databases, indexing, and search infrastructure.
Develop retrieval-augmented generation (RAG) and grounding patterns to improve AI responses.
Build memory and context-management capabilities for conversational and agentic systems.
Design enterprise knowledge ingestion, content indexing, metadata synchronization, and connectors.
Create context orchestration services to supply information to AI applications and agents.
Develop retrieval observability and diagnostics for grounding quality, retrieval quality, and performance.
Apply distributed systems and platform engineering principles to build reliable, scalable, secure stateful services.
Design and operate containerized services on Kubernetes and contribute to multi-region architectures.
Evaluate emerging retrieval and knowledge technologies through research and PoCs.
Lead code reviews, design reviews, testing, debugging, and architecture discussions.
Collaborate with AI platform, application engineering, product, architecture, and data teams.