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

Nordstrom•Seattle, WA

9h•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 a Senior Engineer, you are a lead individual contributor responsible for the quality of a team’s work and capable of tackling complex design and problem solving without supervision. You are a product-minded engineer — you design systems spanning multiple weeks or months of work, hold a strong point of view on what good agent user experience looks like, make technical decisions that balance short and long-term business objectives, and take ownership of team-level costs and metrics. You will champion new techniques, mentor junior engineers, and be a key technical voice in cross-functional discussions.

Requirements

6+ years of professional software engineering experience, with a strong track record of designing and delivering complex, scalable distributed systems.
AI Fluency — Required: Hands-on experience working with LLMs, foundation model APIs (OpenAI, Anthropic, Google, etc.), prompt engineering, retrieval-augmented generation (RAG) architectures, and embedding-based search in production environments.
Experience designing, building, and operating AI agents or agentic workflows in production, including tool-use, orchestration, and integration with downstream systems.
Strong understanding of how to assemble, prune, and structure context for agents to maximize decision quality within token, latency, and cost constraints.
Experience designing evaluation frameworks and safety guardrails for LLM-based systems, including offline benchmarks, online telemetry, and responsible deployment practices.
Familiarity with short-term and long-term memory patterns for agents, vector stores, conversation state, and durable workflow state.
Hands-on experience with agentic frameworks such as Claude Agent SDK, LangGraph, AutoGen, CrewAI, Semantic Kernel, or OpenAI Assistants API.
Familiarity with multi-agent orchestration patterns: task decomposition, tool-use pipelines, and human-in-the-loop workflows.
A product-minded approach to engineering: strong instincts for user impact, comfortable pushing back on requirements when the right solution isn’t the one initially asked for, and able to translate business intent into agentic capabilities.
Proficiency in Python; strong grasp of multiple tech stacks and cloud-native development on AWS and/or GCP.
Experience working with cross-functional teams including product, business, infrastructure, and security stakeholders.
Strong verbal and written communication skills; ability to articulate complex technical decisions to both technical and non-technical audiences.
Agile development experience (Scrum, Kanban, Lean, or similar) with a continuous improvement and quality mindset.
Bachelor’s or Master’s degree in Computer Science, Engineering, or equivalent practical experience.

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 modern CI/CD practices and tools (e.g., GitLab).
Strong emphasis on building observability into systems — real-time alerting, dashboards, metrics, and performance accountability.
Background in retail, e-commerce, or supply chain domains — understanding of how AI agents can drive value in inventory, fulfillment, personalization, or customer service.
Experience with big data technologies (Spark, BigQuery, Redshift) and integrating ML models into production services.
Contributions to open-source AI projects; curiosity and engagement with the broader AI/ML engineering community.

Responsibilities

Partner with business and technology stakeholders to define the “art of the possible” with agents — translating ambiguous problems into agentic solutions with clear success criteria and measurable outcomes.
Design and build core agentic solutions end-to-end across orchestration, tool-use pipelines, and integration with enterprise systems.
Own end-to-end solution design for agentic solutions spanning multiple engineers’ work, with full upstream/downstream integration consideration.
Apply context engineering to determine what an agent sees, when, and why — balancing token economics, latency, and decision quality across RAG patterns, structured retrieval, and dynamic prompt assembly.
Develop and own evaluations and guardrails that demonstrate solutions are safe, reliable, and accurate — offline benchmarks, online production telemetry, and failure-mode analysis.
Architect memory and state management approaches that let agents reason across sessions, users, and workflows — short-term context, long-term memory, and durable conversation state.
Apply AI fluency to integrate LLM APIs, embedding models, vector stores, and agentic frameworks into production services; evaluate and adopt emerging techniques as appropriate.
Make and clearly articulate technical trade-offs between short-term delivery needs and long-term scalability, factoring in design, framework choice, model selection, and infrastructure costs.
Design systems accounting for current and upcoming product cycles, team-level cost responsibility, and alignment with cross-functional roadmaps.
Lead design and code reviews across the team; provide actionable feedback and maintain a high bar for quality, testability, and extensibility.
Design key metrics, evaluations, and observability patterns for agentic solutions; drive accountability for performance, cost, accuracy, and security of feature work.
Work with business, infrastructure, and security teams to deliver enhancements, reliability improvements, and bug fixes for production AI systems.
Surface potential design or delivery conflicts in the current or upcoming product cycle and make clear recommendations on the best path forward.
Mentor and support junior engineers across a wide spectrum of technical activities; participate in hiring interviews with clear, specific feedback.
Ensure own work and team members’ work follows Nordstrom’s engineering and security standards; contribute to those standards.