Top AI & Machine Learning Jobs in Seattle, WA

Designs and deploys production-grade generative AI, LLM, and agentic systems for customer service automation. Responsibilities include multi-agent orchestration, RAG solutions, API and enterprise-tool integration, prompt engineering, model optimization, evaluation frameworks, cloud deployment, security, governance, observability, and reliability engineering. The role collaborates with engineering, product, cybersecurity, and business teams; conducts design and code reviews; mentors engineers; and researches emerging AI technologies.

Leads forward deployment of agentic AI systems within a business unit. Designs and delivers production LLM solutions, including integrations, RAG pipelines, prompt orchestration, multi-agent workflows, data engineering, and performance experiments. Measures ROI, improves data quality, mentors engineers, guides technical delivery, and communicates with business stakeholders. The role requires strong Python, cloud-native deployment, enterprise integrations, responsible AI, and data security expertise.

Develop and maintain forecasting and machine-learning models for marketing performance, budget allocation, and customer acquisition trends. Diagnose model failures and drift using Python, R, and SQL; conduct scenario forecasting, statistical validation, experimentation, and media attribution analysis. Build dashboards in Tableau and Power BI, collaborate with marketing, finance, analytics, and data engineering teams, and scale models into production environments.

Build and deploy agentic AI solutions embedded in business units. Develop integrations with CRM, billing, ticketing, ERP, and other enterprise data sources; create lightweight data pipelines; tune prompts and agent behavior; run production A/B experiments; validate ROI; debug systems; document performance and failure modes; and partner with business stakeholders. The role requires Python, SQL, cloud, Docker, LLM integration, data security, responsible AI, and production engineering experience.

Leads end-to-end delivery of agentic AI systems embedded within business units. Designs LLM applications, RAG pipelines, prompt orchestration, multi-agent workflows, integrations, and data pipelines for production environments. Owns architecture, experimentation, ROI measurement, data quality, stakeholder communication, and domain reporting. Mentors deployed engineers, establishes reusable engineering patterns, supports responsible AI and data security, and guides solutions from prototyping through production hardening.

Own the Responsible AI evaluation framework, evaluation program, and AI transparency standard. Define product vision, strategy, roadmap, features, user stories, and backlogs for AI evaluation tests and benchmarks. Partner with Legal, Privacy, AI engineering, and business stakeholders to prioritize evaluations, coordinate releases, monitor quality, and close findings. The role requires technical experience, product management expertise, Agile SDLC knowledge, and familiarity with Responsible AI frameworks and regulated AI applications.