AI Agentic Solutions Engineer

• 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 Nordstroms engineering and security standards; contribute to those standards

• 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 isnt 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.
• Bachelors or Masters degree in Computer Science Engineering or equivalent practical experience.

• 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

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