Top AI & Machine Learning Jobs in Seattle, WA

Design and develop scalable software systems and production LLM solutions using RAG, agentic workflows, PEFT, and guardrails. Own architecture, technical execution, SDLC activities, cloud and Databricks governance, DevOps automation, testing, deployment, and system reliability. Collaborate with cross-functional teams, conduct code reviews, translate business requirements, and guide software design. The role is client-facing and follows a hybrid work model with travel as needed.

Lead ZAIDYN's AI Engineering function to design, build, and ship an enterprise-grade agentic AI platform. Own architecture, integrations (AWS Bedrock/AgentCore), LLMOps/eval frameworks, observability, governance, and team hiring/operations. Ensure production readiness, cost/latency controls, auditability, and cross-functional delivery with product and QA while representing the platform externally.

The AI Engineering Specialist will oversee project tasks, manage expectations, mentor junior members, and design scalable AI systems while working with advanced AI models and ML pipelines.

Build and optimize production-grade LLM and generative AI systems, including RAG pipelines, model fine-tuning, embedding search, evaluation, and scalable inference. Develop FastAPI services, integrate AI into production software, and improve latency, GPU utilization, reliability, and cost efficiency. Research emerging AI techniques, contribute to MLOps through model tracking and observability, and collaborate with clients and cross-functional teams.

The Lead AI Engineer will design and implement LLM-powered applications, optimize RAG pipelines, and evaluate models while integrating AI systems into production environments.

Lead AI partnership strategy and execution to grow AI Frontier Labs alliances. Own alliance performance, co-sell pipeline, joint GTM and solution development, partner account mapping, enablement, marketing, and executive alignment to scale generative AI adoption and revenue.

Lead architecture, design, and delivery of large-scale GenAI-enabled, distributed systems. Manage and mentor engineering teams, define standards and APIs, integrate LLMs and agentic workflows, ensure production readiness, observability, scalability, and partner cross-functionally with Product, AI/ML, Data Engineering, and DevOps.