Engineer 1: Agentic AI - Inventory Solutions (Hybrid - Seattle, WA)

Nordstrom•Seattle, WA

3d•Hybrid

About The Position

Nordstrom’s Inventory Integration Systems team owns the platforms and data that track in-transit inventory, reconcile audits, capture vendor data, and maintain accurate enterprise inventory across a multi-market, multi-channel business. The team is applying agentic AI to inventory systems including AURA, IAP, OmniTrace, and the PADLC agent pipeline. These production agents investigate inventory problems, correlate signals, and take scoped actions. As an early-career individual contributor, the Engineer 1 builds a foundation in agentic AI engineering while working on production inventory systems under senior engineers’ guidance. The role focuses on well-scoped work such as implementing agent components, writing tests, documenting work, and learning how production agentic systems are designed, evaluated, and operated. It offers an opportunity to develop into an agentic solutions engineer while working on retail supply-chain and inventory challenges.

Requirements

At least 1 year of professional software engineering experience, with internships counting, or a relevant certification such as a bootcamp.
Some hands-on exposure to LLMs or foundation model APIs, through coursework, projects, internships, or professional experience.
Curiosity about agentic AI, including how agents use tools, retrieve context, and take actions, and desire to build production skills in this area.
Solid computer science fundamentals, including data structures, algorithms, and object-oriented design.
Familiarity with RESTful APIs, JSON, and communication between services.
Basic understanding of software testing practices and version control such as Git.
Willingness to learn modern agentic frameworks such as LangGraph, CrewAI, Semantic Kernel, or the Claude Agent SDK.
Strong verbal and written communication skills and a collaborative, feedback-seeking mindset.

Nice To Haves

Coursework, personal projects, or internship experience involving prompt engineering, retrieval-augmented generation (RAG), or vector databases.
Exposure to a backend language and framework such as Java/Spring Boot or Python, and messaging middleware such as Kafka.
Exposure to cloud platforms such as AWS or GCP, containerization such as Docker or Kubernetes, and CI/CD basics.
Familiarity with backend databases such as Spanner or Postgres.
Interest or coursework in retail, e-commerce, supply chain, or inventory problem spaces.
Contributions to open-source AI projects or personal experimentation with LLM applications.

Responsibilities

Implement agent components for inventory solutions, including tool integrations, prompt templates, and retrieval steps, under the direction of senior engineers.
Write clean, well-tested code and clearly document work.
Learn and apply context engineering to ground agents in inventory information such as audit records, ledger state, and vendor data.
Help build and run evaluation scripts and offline benchmarks to assess agent safety, accuracy, and reliability.
Participate in on-call rotations and design reviews, debug issues, and surface trade-offs for owned functions or components.
Add basic logging, metrics, and alerts to systems.
Follow engineering and security standards, including secure handling of sensitive inventory and vendor data.
Collaborate with engineering, product, and business stakeholders to understand the inventory platform and identify ways agentic AI can reduce manual audit and reconciliation work.