Principal Engineer – Store Optimization

Target Target · Retail · NCD-0375 Brooklyn Park, MN

Principal Engineer role focused on defining and driving unified architecture for a store optimization platform. This involves leading the evolution towards intelligent task orchestration, establishing multi-objective optimization frameworks, and ensuring end-to-end system integrity. The role requires embedding observability, guardrails, and explainability into AI-driven systems, and partnering across teams to drive scalable platform design. Experience building and productionizing AI/GenAI systems, including LLMs, RAG, prompting, evaluation, and guardrails, is critical.

What you'd actually do

  1. Define and drive unified architecture across Task Ingestion, Task Prioritization, Task Optimization, and Routing
  2. Lead the evolution toward goal-driven, intelligent task orchestration aligned to business outcomes (e.g., labor efficiency, sales recovery, SLA adherence)
  3. Establish multi-objective optimization frameworks to balance competing business priorities
  4. Simplify and standardize shared capabilities to reduce duplication and technical debt
  5. Ensure end-to-end system integrity across interconnected workflows

Skills

Required

  • 4-year degree or equivalent experience
  • 12+ years of software engineering experience
  • 4+ years setting technical strategy across teams or domains
  • Deep expertise in distributed systems
  • event-driven architecture (Kafka)
  • scalable platform design
  • Strong hands-on experience with Java/Kotlin or Python
  • building API-first services (Spring Boot or Micronaut)
  • Experience designing and operating cloud-native systems using Kubernetes and Docker
  • Strong understanding of data systems (PostgreSQL, MongoDB, Redis)
  • observability (metrics, logging, tracing)
  • Experience building and integrating AI/GenAI systems (LLMs, RAG, prompting, evaluation, guardrails)
  • Proven ability to productionize AI systems, including performance, reliability, and enterprise integration
  • Strong ability to translate business goals into technical strategy
  • drive cross-team architectural alignment
  • Proven leadership and communication skills
  • ability to influence senior stakeholders

Nice to have

  • Experience with optimization, routing, or decisioning systems at scale
  • Familiarity with graph algorithms, heuristics, or tools like OR-Tools
  • Experience with agentic workflows, multi-agent systems, or human-in-the-loop orchestration
  • Experience with GenAI frameworks (e.g., LangChain, LlamaIndex)
  • vector databases
  • semantic search
  • Exposure to graph databases (e.g., Neo4j)
  • knowledge graphs
  • streaming data systems

What the JD emphasized

  • productionize AI systems
  • enterprise integration
  • AI/GenAI systems (LLMs, RAG, prompting, evaluation, guardrails)
  • intelligent task orchestration
  • multi-objective optimization frameworks
  • observability, guardrails, and explainability

Other signals

  • productionize AI systems
  • enterprise integration
  • intelligent task orchestration
  • multi-objective optimization frameworks
  • AI/GenAI systems (LLMs, RAG, prompting, evaluation, guardrails)