Top Tech Jobs & Startup Jobs in Seattle, WA

Own and manage a digital commerce product area end to end, focusing on top-of-funnel experiences such as browsing, search, and product pages. Analyze APIs, microservice contracts, data flows, backend batch jobs, page data, and analytics tags. Apply agile digital operations using Azure DevOps, perform basic financial analysis, and work with digital retail technologies including search platforms, CMS, APIs, and service layers.

Design, develop, test, deploy, and maintain scalable Java backend applications using Spring Boot and microservices. Build RESTful and SOAP integrations, optimize performance and reliability, implement security controls, and develop relational and NoSQL database solutions. Collaborate with architects, product owners, and frontend teams; participate in design reviews and architectural decisions; troubleshoot production issues; and uphold DevOps, CI/CD, coding, and Agile practices. Mentor junior developers and guide technical discussions across enterprise development teams.

Lead end-to-end product design for web and mobile, collaborating with PMs, engineers, and researchers. Conduct user research and testing, create wireframes, prototypes, and design systems, ensure accessibility and data-informed decisions, and support implementation to deliver scalable eCommerce experiences.

Lead planning, execution, and delivery of enterprise technology projects. Manage scope, timelines, budgets, resources, risks, vendors, stakeholders, and communications across Agile, Scrum, and Waterfall environments. Oversee software development, technology implementations, cloud transformation, application modernization, and concurrent project priorities while ensuring business and technology objectives are achieved.

Build and maintain production-grade full-stack applications across frontend, backend, cloud infrastructure, and deployment pipelines. Develop responsive React or similar frontend applications, TypeScript and Node.js backend services, RESTful APIs, and AWS cloud-native architectures. Design serverless and container-based solutions, implement Generative AI and RAG applications, and work with vector databases, embeddings, retrieval pipelines, model evaluation, and prompt engineering. Own projects from design through deployment with minimal oversight.

Design, develop, and scale backend services and agent orchestration workflows for an Agentic AI platform. Integrate LLMs and vector DBs, implement RAG pipelines, build tools/connectors, ensure CI/CD, monitoring, testing, and optimize performance for production AI deployments.

Develop and maintain Python backend services and AI platform components. Build agent orchestration workflows, integrate LLMs and RAG solutions, connect tools and enterprise systems, optimize performance, and create testing and evaluation frameworks for production-ready agentic AI deployments.

Develop production-grade agentic AI applications in Python, including AI agents, multi-agent workflows, APIs, integrations, RAG pipelines, and cloud deployments. Use LLMs, vector databases, agent frameworks, and observability tools while implementing secure, governed AI solutions. Collaborate with clients, architects, product teams, and data scientists through workshops, demonstrations, proofs of concept, and troubleshooting sessions.

Develop scalable Java full-stack applications using Spring Boot, REST APIs, microservices, React or Angular, databases, cloud platforms, Docker, and Kubernetes. Build and integrate generative and agentic AI capabilities, including LLMs, RAG, vector databases, orchestration, tool calling, and MCP. Partner with enterprise stakeholders to prototype, deploy, secure, monitor, and support production solutions while translating ambiguous business needs into technical implementations.

Lead and mentor QA team to define QA strategy and processes. Design, develop, and maintain Java-based Selenium/Cucumber automation frameworks, integrate tests into CI/CD pipelines, test cloud-hosted AWS applications, manage defect lifecycle, define test plans and metrics, and collaborate with DevOps, development, and product teams to ensure software quality across the SDLC.