Top Tech Jobs & Startup Jobs in Seattle, WA

Lead technical project success for enterprise AI infrastructure engagements. Collaborate with engineers, clients, and account leaders to capture requirements, decisions, dependencies, risks, and tradeoffs. Track constraints involving security, compliance, data sovereignty, procurement, and restricted environments. Maintain project visibility, align deliverables with business objectives, resolve issues, support implementation-to-operations handoffs, and identify expansion and renewal opportunities. Translate complex technical concepts for engineering, business, and executive stakeholders.

Build and operate a secure Kubernetes-based AI/ML platform for government test and evaluation teams. Responsibilities include GPU scheduling, workload orchestration, infrastructure-as-code, GitOps, observability, capacity planning, upgrades, security hardening, compliance support, and operational documentation. The role involves restricted and disconnected environments, automated security tooling, collaboration with government and engineering stakeholders, technical leadership, and mentoring.

Own the end-to-end architecture of a secure AI testing and evaluation platform for Government customers. Define technical roadmaps, interfaces, standards, and design patterns; evaluate open-source and commercial technologies; guide security, interoperability, scalability, and reliability decisions; and support deployment in restricted environments. Advise stakeholders, coordinate multiple engineering disciplines, document technical risks and tradeoffs, support proposals, and mentor engineers and technical leads.

Design, optimize, and deploy deep learning models for customer behavior prediction. Perform feature engineering, implement research-informed architectures, scale training pipelines, collaborate with client teams, document findings, and uphold engineering best practices.

Lead design and delivery of large-scale open-source AI solutions: translate business needs into architectures, advise on ML frameworks and MLOps, support pre-sales, engage clients and open-source communities, mitigate technical risks, and guide development teams to production.