Sr. Engineer, AI Agentic Platform

Confiz•Seattle, WA

1d•Hybrid

About The Position

One of Confiz's largest retails clients is investing in AI as a core driver of retail innovation, and the AI Agentic Platform team is building the shared foundation that makes it possible. Our platform enables AI agents to autonomously reason, use tools, coordinate across systems, and take action on behalf of customers and business teams, powering experiences across commerce, personalization, inventory, and customer service. As 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 will design systems spanning multiple weeks or months of work, 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

Bachelor's or Master's degree in Computer Science, Engineering, or equivalent practical experience.
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.
Platform engineering in AI teams — You have contributed to or built shared AI infrastructure or developer platforms within an AI-focused product organization, enabling other teams to build reliable AI-powered features on top of your foundation.
Proficiency in Python and/or Java; strong grasp of multiple tech stacks and cloud-native development on AWS and/or GCP.
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).
Demonstrated ability to design systems spanning multiple weeks or months of work, incorporating a full team’s worth of engineers, and balancing short and long-term trade-offs.
Strong emphasis on building observability into systems — real-time alerting, dashboards, metrics, and performance accountability.
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.

Responsibilities

Design and build core AI Agentic Platform capabilities, including agent orchestration layers, tool-use pipelines, memory systems, and APIs that enable product teams across the company to deploy AI agents.
Own end-to-end solution design for platform components spanning multiple engineers’ work, with full upstream/downstream integration consideration.
Apply AI fluency to integrate LLM APIs, embedding models, vector stores, and RAG patterns into platform services; evaluate and adopt emerging agentic frameworks as appropriate.
Make and clearly articulate technical trade-offs between short-term delivery needs and long-term platform scalability, factoring in design, component choice, 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, events, and observability patterns for platform components; drive accountability for performance 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 the company’s engineering and security standards; contribute to those standards.