Top Tech Jobs & Startup Jobs in Seattle, WA

Lead platform engineering strategy across multi-environment and multi-region cloud systems. Architect Kubernetes and container platforms, establish infrastructure-as-code standards, and implement DevSecOps, security, compliance, reliability, observability, and cost optimization practices. Lead cloud migrations and AI/ML infrastructure initiatives involving model serving, GPU orchestration, LLM gateways, vector stores, and CI/CD. Mentor engineers, guide architecture decisions, communicate with stakeholders, and serve as a technical escalation point.

Leads large-scale AI, digital, and cloud transformation programs from strategy through launch and realized business value. The role coordinates concurrent Agile teams, technical direction, architecture discussions, AI lifecycle governance, risk management, KPIs, stakeholder communication, staffing, and executive decision-making. It requires translating business goals into technical plans, managing complex dependencies and trade-offs, mentoring delivery leaders, and shaping delivery practices while partnering with clients on growth opportunities.

Design, build, deploy, and maintain production AI/ML systems, including models, data pipelines, training workflows, agentic systems, and RAG pipelines. Monitor and optimize reliability, scalability, performance, and costs while contributing to architecture decisions. Collaborate with product, engineering, and data teams, communicate technical tradeoffs, support junior engineers, and apply responsible AI, security, observability, and containerization practices.

Lead large-scale AI, digital, and cloud transformation programs from strategy through launch and realized business value. Manage multiple Agile teams, workstreams, dependencies, governance, KPIs, budgets, risks, and executive stakeholder alignment. Translate business needs into technical requirements, facilitate architecture discussions, establish AI lifecycle and evaluation standards, and guide decisions involving model risk, observability, and agentic solutions. Mentor delivery managers, support staffing and hiring decisions, and contribute to client expansion opportunities and delivery practice improvements.

Lead the architecture, development, deployment, and adoption of production AI/ML systems within client environments. Build scalable ML platforms, agentic systems, RAG pipelines, and integrations; resolve complex enterprise deployment challenges; and establish standards for reliability, security, cost, and responsible AI. Partner with technical and business stakeholders, support client enablement, lead design reviews, and mentor engineers while delivering solutions from research through production.

Leads the architecture, development, optimization, and production delivery of complex AI/ML and agentic systems. Builds scalable ML platforms, pipelines, and infrastructure; establishes engineering standards; drives AI strategy; and mentors engineers. The role requires expertise in generative AI, AWS, RAG, evaluation and observability, AI safety, API integration, cost optimization, Docker, Kubernetes, and AI coding tools.

Leads architecture and delivery of production-grade AI, multi-agent, cloud, data, and ML systems. Defines AWS-centered technical strategy, builds agentic platforms, designs data pipelines and MLOps infrastructure, and establishes DevOps, security, governance, observability, and cost standards. Serves as the senior technical authority for clients and cross-functional teams, mentors engineers, evaluates emerging technologies, and drives complex solutions from research through production.

Lead design and delivery of enterprise generative and agentic AI systems on AWS. Architect scalable, secure Bedrock- and SageMaker-based platforms, validate via Python prototypes, enforce AWS Well-Architected and security best practices, mentor delivery teams, and drive business-aligned production deployments and reusable accelerators.

Build and operate production machine learning training systems on Amazon SageMaker using AWS Trainium. The role focuses on optimizing PyTorch code for Trainium hardware, understanding NeuronCore compilation, diagnosing device-level issues, tuning distributed training for cost and throughput, and delivering production pipelines. The engineer will collaborate directly with client and internal teams in a forward-deployed engineering environment.

Lead design and delivery of enterprise generative and agentic AI systems on AWS. Architect Bedrock-based RAG and multi-agent platforms, prototype in Python, ensure security, observability, IaC, and guide clients from discovery to production.