One of Confiz's largest retail clients is seeking an experienced
AI Agentic Solutions Engineer to serve as a lead individual contributor responsible for driving technical excellence and the overall quality of the team's work. This role requires the ability to independently tackle complex technical design and problem-solving challenges while maintaining a strong product mindset.
The ideal candidate will design and deliver scalable systems spanning multiple weeks or months of development, establish a strong point of view on effective agent user experiences, and make thoughtful technical decisions that balance short- and long-term business objectives. This individual will also take ownership of team-level costs, performance metrics, and technical outcomes.
In addition, the AI Agentic Solutions Engineer will champion emerging technologies and engineering practices, mentor junior engineers, and serve as a key technical voice in cross-functional discussions with product, business, infrastructure, and security stakeholders.
This is a hybrid role based in Seattle, WA. Onsite presence of 4 days per week is expected with an 8:00 am - 5:00 pm work schedule.
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
Qualifications - 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 Have
- 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
