Top AI Engineer Jobs in Seattle, WA

Designs, builds, tests, and troubleshoots full-stack cloud-native applications on AWS. Translates client needs into technical solutions across architecture, development, testing, and delivery; builds scalable infrastructure and integration patterns; applies AI-enabled engineering methods; and supports modernization initiatives. The role also reviews technical work, mentors junior engineers, collaborates with architecture teams, and helps deliver cloud, data, and analytics solutions.

Leads AWS-native application design, development, testing, modernization, and architecture efforts. Manages technical workstreams, delivery timelines, scope, and team allocation while coaching engineers on cloud infrastructure, AI-native engineering, testing, and troubleshooting. Reviews technical designs and code, coordinates application and infrastructure activities, supports client architecture discussions, and improves delivery reliability and speed.

Architects the data and AI foundation for marketing, sales, and analytics. Responsibilities include building Databricks pipelines, Gold Layer tables, semantic models, documentation, AI-enabled data products, and production LLM or agent systems. The role leads cross-functional initiatives, translates ambiguous business needs into scalable solutions, ensures data quality and reliability, mentors engineers, automates workflows, and partners with BI, data science, marketing, sales, and R&D teams.

Develop and implement advanced machine learning algorithms and models for Atlassian products. Design system and model architectures, conduct experiments and evaluations, collaborate with product, engineering, and analytics teams, and mentor emerging machine learning engineers. The role also contributes to shared AI infrastructure and capabilities supporting search, knowledge discovery, and conversational experiences across the company.

Lead technical strategy and development of Confluence AI experiences across frontend, backend, and AI integrations. Design scalable systems, apply generative AI, retrieval, agents, evaluation, and safeguards, and improve product quality, adoption, trust, and customer value. Drive complex initiatives from architecture through launch, partner cross-functionally, mentor engineers, establish quality practices, and guide experimentation and continuous improvement across Atlassian.

Develop and productionize machine learning systems for search, retrieval, ranking, recommendations, conversational experiences, and AI agents. Own projects across the ML lifecycle, build evaluation datasets and benchmarks, conduct offline and online evaluations, analyze model quality, and improve system performance. Design scalable architectures addressing reliability, latency, privacy, and cost. Collaborate across product and engineering teams, communicate technical decisions, mentor engineers, and contribute to ML best practices.

Lead enterprise AI engagements from opportunity discovery through production deployment, adoption, measurement, and continuous improvement. Design scalable AI systems, evaluate models and agentic architectures, establish engineering and responsible AI standards, and create reusable frameworks. Partner with executives and business teams, translate ambiguous problems into measurable outcomes, influence technical direction, and coach senior engineers and emerging technical leaders.

Lead and coach teams to design, develop, and deliver production-grade AI and data solutions for clients. Manage client accounts, ensure quality and timelines, analyze complex datasets, implement best practices, and drive continuous improvement while fostering client relationships and technical excellence.

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.

Design and build MLOps infrastructure for production deployment of data, machine-learning models, and software. Develop end-to-end ML pipelines, CI/CD/CT workflows, secure repository strategies, containerized deployments, and automation for Edge AI applications. Collaborate with developers, data scientists, and ML engineers to define tooling, monitor DevOps performance, and improve operational efficiency. Support embedded-system CI/CT, open-source pipelines, and model testing.

Owns end-to-end delivery of production agentic AI solutions for a business domain, including requirements discovery, LLM architecture, RAG pipelines, integrations, data engineering, experimentation, monitoring, and ROI validation. Leads relationships with business stakeholders, mentors forward-deployed engineers, and establishes responsible AI, privacy, security, and governance practices. The role also communicates outcomes to senior leadership and improves FDE delivery standards.

Leads global teams developing production AI, GenAI, RAG, agentic workflow, and MLOps solutions for health systems and health plans. Translates clinical, financial, and operational challenges into HIPAA-compliant use cases involving clinical decision support, population health, revenue cycle, research, and optimization. Oversees healthcare data science engagements, model governance, executive stakeholder communications, client delivery, and team development.

Develop AI agents, copilots, full-stack applications, backend services, and enterprise integrations for internal business workflows. Build React and TypeScript interfaces, automate data processing, integrate LLM APIs, write tests, troubleshoot defects, and support deployments. Collaborate with engineers and business stakeholders across Finance, Supply Chain, HR, Legal, and data center operations while following security, monitoring, documentation, and code-quality practices.

Own an AI portfolio within a business domain, identify and prioritize opportunities, and build agentic solutions from prototype through production. Establish reusable patterns, evaluations, traces, and human-in-the-loop controls for increasing autonomy. Partner with business leaders, measure portfolio-level impact, and build production-grade applications across frontend and backend systems. Mentor engineers, review designs and code, influence platform choices, and communicate effectively with operators, engineers, and executives.

Architect, build, deploy, and scale production AI agents and platforms for legal workflows. Develop secure, reliable, and cost-effective AI solutions; prototype and evaluate agents; optimize performance; redesign business processes; and collaborate with Legal, Data, Security, Privacy, and Engineering teams. Contribute to AI training, governance, responsible AI adoption, and enterprise standards while translating technical capabilities into measurable business outcomes.

Build and deploy AI-powered tools for Rula’s business and clinical teams by observing workflows, rapidly delivering production software, measuring usage and impact, and enabling teams to operate tools independently. The role requires privacy-by-design practices, rigorous AI evaluation, customer-centric discovery, and integration with complex legacy systems. Staff engineers may optimize LLM costs and quality while creating reusable building blocks for future teams.

Lead architecture and hands-on delivery of production-grade LLM and agentic AI platforms: design cloud-native AWS services, retrieval and RAG, observability, evaluation frameworks, secure APIs, performance/cost optimization, and mentor senior engineers while defining engineering standards for reliable, safe AI operations.

Architect and lead enterprise AI infrastructure supporting LLM training and inference, machine learning, agentic AI, and production platforms. The role covers HPC, GPUs, rack-scale systems, private and hybrid cloud, Kubernetes, networking, storage, observability, security, reliability, governance, and automation. Responsibilities include solving complex scalability and performance challenges, defining engineering standards, evaluating emerging technologies, leading cross-functional initiatives, and mentoring engineers in a regulated environment.

Design, build, and operate scalable services and distributed systems supporting agentic AI capabilities. Lead system, API, and data architecture; integrate AI/ML solutions across development and production environments; establish standards for reliability, security, testing, observability, and compliance; partner cross-functionally on technical direction; and mentor engineers while building reusable AI tools, orchestration layers, and safeguards.

Lead the technical direction, architecture, and development of Compass, an LLM-powered agent platform. Design multi-agent systems, MCP and A2A integrations, orchestration, evaluation, and observability infrastructure. Drive cross-team technical projects, production support, engineering quality, mentoring, and adoption of AI-assisted development practices. Partner with Product, QA, Design, Operations, and Engineering stakeholders to deliver reliable agentic systems.

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.

Leads engineering for enterprise agentic AI and LLM platforms. Designs and deploys production-grade LLM services, RAG solutions, autonomous agents, APIs, and cloud-native AWS architectures. Provides technical leadership, architecture guidance, code reviews, MLOps, monitoring, governance, and operational excellence. Drives responsible AI practices and adoption of AI-assisted development across teams, emphasizing security, auditability, human oversight, scalability, and performance.

Build complete applications, workflow automations, and basic AI agents using AI coding agents. Translate customer needs into specifications, deploy solutions, instrument usage and outcomes, evaluate AI-generated output for security and quality, engage with customers, and document effective prompts and development patterns.

Build complete applications, workflow automations, and basic AI agents by directing AI coding agents. Translate customer needs into specifications, deploy full-stack solutions, instrument usage and outcomes, evaluate AI-generated work for security and reliability, engage with customers, and document effective prompts and development patterns.

Lead the AI Quality engineering team while remaining hands-on building evaluation infrastructure, observability tooling, diagnostic systems, datasets, annotation workflows, and CI/CD pipelines for production AI agents. Run experiments across prompts, models, routing, caching, and agent harnesses to improve quality, latency, and cost. Partner with AI Core engineering and manage technical direction and team priorities.