Principal Engineer: AI Agentic Platform (hybrid - Seattle, Wa)

Nordstrom Nordstrom · Retail · Seattle, WA

Principal Engineer to lead the design, architecture, and development of an AI Agentic Platform enabling autonomous reasoning, action, and collaboration across Nordstrom's technology ecosystem. This role focuses on building foundational layers for agent orchestration, tool use, memory, and multi-agent coordination at scale, integrating LLM APIs, vector stores, RAG, and agentic frameworks.

What you'd actually do

  1. Architect and evolve the AI Agentic Platform — defining the foundational layers for agent orchestration, tool use, memory, and multi-agent coordination at Nordstrom scale.
  2. Partner with AI/ML and product teams to translate business goals into platform capabilities that enable autonomous agent workflows across commerce, inventory, and customer experience domains.
  3. Lead cross-team design reviews, establish platform engineering standards, and create reusable architecture artifacts that accelerate delivery across the organization.
  4. Apply deep AI fluency to evaluate and integrate LLM APIs, vector stores, RAG patterns, and emerging agentic frameworks (e.g., LangGraph, AutoGen, OpenAI Assistants API, Semantic Kernel).
  5. Drive decisions on platform observability, evaluation, and guardrails — ensuring agents behave reliably, safely, and within business policy constraints.

Skills

Required

  • 10+ years of professional software engineering experience, with a track record of designing and delivering large-scale distributed platforms.
  • Deep, hands-on experience with LLMs, foundation model APIs (OpenAI, Anthropic, Google, etc.), prompt engineering, RAG architectures, and embedding-based search in production environments.
  • Built or significantly contributed to shared AI infrastructure or developer platforms within an AI-focused product organization, enabling multiple teams to build on top of your foundation.
  • Strong proficiency in Python and/or Java; experienced with cloud-native development on AWS and/or GCP.
  • Expertise architecting complex, scalable distributed systems — microservices, event-driven architectures, API gateways, and data pipelines.
  • Highly skilled at applying architectural patterns to express complex, scalable systems that optimize for simplicity, operational support, and implementation velocity.
  • Demonstrated experience influencing senior technical and business leaders, with the ability to clearly articulate engineering trade-offs and drive cross-team decisions.
  • Strong DevOps foundation — CI/CD (GitLab or equivalent), containerization (Kubernetes/Docker), observability and telemetry, and SLA-driven reliability engineering.
  • Experience with Agile methodologies, continuous improvement mindset, and security-first development practices.
  • Track record of mentoring and growing engineering talent across multiple teams.
  • Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field.

Nice to have

  • Hands-on experience building AI Agentic systems — designing and deploying autonomous agents using frameworks such as LangGraph, AutoGen, CrewAI, Semantic Kernel, or OpenAI Assistants API.
  • Familiarity with multi-agent orchestration patterns: task decomposition, tool-use pipelines, agent memory (short-term and long-term), and human-in-the-loop workflows.
  • Experience with agent evaluation frameworks, safety guardrails, and responsible AI deployment in production at scale.
  • Background in retail, e-commerce, or supply chain — understanding of how AI agents can power inventory, fulfillment, personalization, or customer service workflows.
  • Contributions to open-source AI or platform projects; active participant in the broader AI/ML engineering community.
  • Experience with data science-enabled solutions, big data technologies (Spark, BigQuery, Redshift), and integrating ML models into production platforms.

What the JD emphasized

  • AI Fluency — Required: Deep, hands-on experience with LLMs, foundation model APIs (OpenAI, Anthropic, Google, etc.), prompt engineering, RAG architectures, and embedding-based search in production environments.
  • Platform engineering in AI teams — You have built or significantly contributed to shared AI infrastructure or developer platforms within an AI-focused product organization, enabling multiple teams to build on top of your foundation.

Other signals

  • AI Agentic Platform
  • autonomous AI
  • agent orchestration
  • tool use
  • multi-agent coordination
  • LLM APIs
  • vector stores
  • RAG patterns
  • agentic frameworks