Engineer 2: AI Agentic Solutions (Hybrid - Seattle, WA)

Nordstrom•Seattle, WA

3d•$121,500 - $188,500•Hybrid

About The Position

Nordstrom is investing in AI as a core driver of retail innovation, and the AI Agentic Solutions team is the pillar responsible for taking that investment from idea to impact. We define the “art of the possible” with agents — partnering with business and technology teams to identify where agentic AI can meaningfully change how Nordstrom operates, then designing and building those solutions end-to-end across commerce, personalization, inventory, and customer service. Our team sits at the intersection of four disciplines: agent engineering, context engineering, evaluations and guardrails, and memory and state management for agentic solutions. As an Engineer 2, you are a solid individual contributor with growing ownership over agentic components and features. You're a subject matter expert within your specific piece of the solution, capable of designing the interaction between multiple modules, and comfortable operating with minimal supervision. You'll partner closely with senior engineers to build production agentic workflows and start developing your own point of view on context engineering, evaluations, and agent design.

Requirements

2+ years of professional software engineering experience.
AI Fluency — Required: Hands-on experience working with LLMs or foundation model APIs (OpenAI, Anthropic, Google, etc.), including some exposure to prompt engineering or retrieval-augmented generation (RAG) patterns.
Some experience building or contributing to AI agents or agentic workflows — tool-use, orchestration, or integration with downstream systems — whether in production, side projects, or coursework.
Growing understanding of how to assemble and structure context for agents within token, latency, and cost constraints, and exposure to evaluation or testing practices for LLM-based systems, including offline benchmarks or basic production monitoring.
Solid CS fundamentals — data structures, algorithms, and object-oriented design — plus at least one production-grade tech stack and working knowledge of cloud-native development on AWS and/or GCP.
Familiarity with modern agentic frameworks such as LangGraph, CrewAI, Semantic Kernel, the Claude Agent SDK, or OpenAI Assistants API.
Strong verbal and written communication skills; comfortable explaining technical work to teammates and cross-functional partners.

Nice To Haves

Experience with RESTful services, event-driven architectures, and backend databases (SQL, NoSQL, or cloud-native datastores).
Familiarity with containerization technologies (Kubernetes, Docker) and CI/CD practices.
Interest in building observability into systems — real-time alerting, dashboards, and metrics.
Background or interest in retail, e-commerce, or supply chain problem spaces.
Exposure to big data technologies (Spark, BigQuery, Redshift) or integrating ML models into production services.
Contributions to open-source AI projects or active engagement with the broader AI/ML engineering community.

Responsibilities

Design and build agentic components with minimal supervision — tool-use integrations, retrieval steps, and orchestration logic — considering how they interact with other modules in the system, writing clear, concise, well-tested code along the way.
Contribute to context engineering work: helping determine what an agent sees, when, and why, within token, latency, and cost constraints.
Apply AI fluency to integrate LLM APIs, embedding models, vector stores, and agentic frameworks into production services, and build the evaluations and guardrails — offline benchmarks and online telemetry — that demonstrate those components are safe, reliable, and accurate.
Participate in on-call rotations, using debugging and profiling tools to resolve issues across the team, and contribute to root-cause analysis on difficult problems.
Learn to lead work processes and design reviews — including reviewing the work of other engineers — and help teammates think through trade-offs on the pieces you know best.
Understand how to log events and publish metrics for the systems you own, and model good practices for access control and sensitive data handling.
Understand business metrics for the team and how your work connects to the wider AI Agentic Solutions roadmap, partnering with business, infrastructure, and security teams to deliver enhancements and bug fixes for production systems.