Top AI Engineer Jobs in Seattle, WA

Owns the design, deployment, governance, and operation of enterprise AI platforms and employee-facing agents. Builds secure control layers including identity, access, device, tenant, MCP, secrets, logging, rate limiting, and cost controls. Establishes AI lifecycle processes, evaluates and rolls out new tools, operates infrastructure to SRE standards, and develops telemetry, dashboards, runbooks, and incident response. Partners with Security, Legal, Privacy, and IT teams to translate governance requirements into enforceable technical controls.

Architect and build reliable, scalable AI platform services supporting model training and inference across hybrid-cloud environments. Develop reusable APIs, SDKs, tooling, and reference architectures; establish observability, resilience, security, automation, and cost-optimization standards. Partner with engineering and product teams to deliver platform capabilities, meet reliability targets, resolve production incidents, and drive remediation. Mentor engineers and promote responsible, secure use of AI-assisted development tools.

Designs, develops, and troubleshoots secure, scalable ML systems and services. Builds production software in Python using ML frameworks, cloud-native technologies, distributed systems, microservices, and NoSQL databases. Creates architecture artifacts, analyzes large datasets, improves system reliability and performance, and uses AI-assisted development tools responsibly. Preferred experience includes GPU workloads in Kubernetes, model-serving frameworks, distributed inference, model compression, and edge deployment.

Lead the design, development, and operation of an enterprise GenAI serving platform. Build high-throughput LLM inference, gateway, routing, GPU optimization, quantization, distributed serving, and observability capabilities. Establish reliability practices, SLOs, performance benchmarks, cost controls, and secure production standards. Drive adoption of agentic AI-enabled engineering workflows with governance, auditability, and human validation while influencing senior stakeholders and enabling reusable platform components across teams.

Design, build, and operate AI-enabled applications and ML/LLM platforms. Deliver production-quality code, SDK/REST integrations, MLOps pipelines, model/versioning and CI/CD, distributed fine-tuning and evaluation, and reusable platform components. Drive adoption of agentic AI workflows, enforce responsible AI and security controls, automate reliability, and influence senior stakeholders to align platform roadmaps with business outcomes.

Hands-on technical leader who designs, builds, and deploys production-grade AI automation and agentic workflows. Responsibilities include rapid prototyping, RAG and document-intelligence systems, API integrations, orchestration, monitoring, reusable AI assets, and mentoring a small engineering team to deliver frequent releases and measurable business impact.

Supports AI engineering initiatives at Vantor, contributing to spatial intelligence solutions and helping develop technologies that enable customers to understand current and future conditions. The posting provides limited role-specific responsibilities but emphasizes problem-solving, innovation, collaboration, and mission-focused work. Applicants must qualify as a U.S. Person under applicable export control and ITAR requirements.

Lead design and build of an AI-native marketing platform: integrations, multi-agent orchestration, UIs, tooling, analytics, CI/CD, auth/multi-tenancy, and production AI agents to automate marketing workflows and drive adoption across teams.

Design, build, and operate autonomous AI agents that automate SRE, DevOps, and DBA workflows. Develop safe agent architectures with planning, tool use, error recovery, evaluation, auditability, and human escalation. Integrate agents with Kubernetes, Terraform, cloud APIs, databases, monitoring platforms, and CI/CD systems. Partner with infrastructure teams to identify automation opportunities and shape Airwallex’s infrastructure AI strategy.

Build and operate production-grade AI developer tooling, agentic workflows, and shared infrastructure that improve software development across engineering teams. Responsibilities include AI-assisted code review, testing, issue triage, model integrations, evaluations, monitoring, cost controls, adoption enablement, and cross-functional collaboration with security, platform, quality, product, and design teams.

Develops scalable automation, AI, machine-learning, and data-analysis solutions for quantum-device research. Responsibilities include building experimental data pipelines, applying AI to device characterization and tuning, integrating models into simulation and control environments, creating reproducible workflows, and communicating technical solutions. The role collaborates across quantum research, engineering, software, and AI disciplines, with opportunities to build AI agents and copilots for experiment setup, log triage, reporting, and knowledge retrieval.

Lead design and build of a global AI/ML deployment platform: architect control plane, multi-region deployments, APIs, autoscaling, and integrations. Own technical direction, engage clients, drive governance of AI-assisted development, and ensure resilient, secure production-grade infrastructure.

Design, build, and operate scalable cloud platform services for deploying, monitoring, governing, and managing AI models and workloads. Lead architecture, reliability, security, observability, automation, and developer self-service initiatives using distributed systems and SRE practices. Collaborate across engineering, product, and research teams, analyze production performance, drive continuous improvement, and mentor engineers while influencing technical strategy.

Designs and builds production computer-use AI agents that combine LLM reasoning with deterministic execution. Develops backend services, workflow state management, recovery, verification, evaluations, and diagnostics. Partners directly with customers to prototype and improve automation, shapes architecture and technical direction, evaluates models and tools, mentors engineers, conducts design reviews, and supports hiring. Requires hands-on experience operating complex backend and LLM-powered systems in production.

Develop scalable full-stack web applications and AI user experiences for Rakuten’s ecosystem. Build front-end interfaces with modern JavaScript frameworks, back-end services and REST APIs, agentic AI frameworks using tools such as LangChain, database schemas, and third-party integrations. Write automated tests, debug systems, optimize performance, and contribute to flexible AI foundation technologies while learning through mentorship.

Own end-to-end development of production-scale AI-powered applications that improve internal workflows and customer experiences. Architect agentic systems, RAG applications, tool-using assistants, evaluation and monitoring platforms, shared APIs, guardrails, and infrastructure. Identify high-leverage opportunities, establish technical standards, influence cross-functional direction, mentor engineers, and operate effectively in a greenfield environment.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Build production agentic AI services for Azure customer support. Responsibilities include designing AI service architectures, developing multi-agent workflows and RAG systems, integrating Azure data sources, creating evaluation frameworks, and writing production-quality C# and Python. The role also covers CI/CD deployment, observability, security, responsible AI, testing, monitoring, incident response, and cross-team delivery.

Designs, implements, and owns production-scale inference systems for large transformer and multimodal models. Responsibilities include optimizing CUDA kernels, inference frameworks, batching, caching, quantization, and serving infrastructure; deploying models; monitoring latency SLOs; responding to incidents; and partnering with research teams. The role also drives technical design and establishes inference engineering standards across the AI Infrastructure team.

Design and optimize multi-agent AI systems for NVIDIA’s CUDA ecosystem, including agent runtimes, compiler-integrated orchestration, evaluation frameworks, and GPU-centric performance solutions. Collaborate across hardware, software, algorithms, compilers, kernels, frameworks, distributed training, and inference teams to accelerate agent planning, tool use, code generation, and other AI workloads.

Design, build, and operate globally distributed AI infrastructure that routes requests, prioritizes traffic, and allocates GPU capacity. Develop reliable, secure, and scalable services; collaborate with product and partner teams to launch AI capabilities; support production systems through monitoring, on-call rotations, troubleshooting, and gradual rollouts. Mentor engineers, conduct code reviews, and apply AI tools throughout the software development lifecycle.

Leads the AI engineering team and develops Parametric’s GenAI platform. Responsibilities include managing engineers, translating business needs into AI solutions, designing reusable platform services and APIs, implementing RAG, prompt engineering, fine-tuning, agentic workflows, and building evaluation and monitoring frameworks. The role also oversees AWS deployments, code and design reviews, production support, model optimization, governance, and the complete AI development lifecycle.

Build and operate production AI agents that automate multi-step workflows across internal teams. Responsibilities include integrating agents with internal systems and third-party tools, deploying on Amazon Bedrock AgentCore, creating evaluation and observability frameworks, implementing guardrails, measuring impact, and establishing reusable agent-development patterns. The role also requires partnering with nontechnical teams, translating ambiguous workflows into reliable automation, documenting solutions, and driving adoption.

Design, build, and operate large-scale, highly available Responsible AI cloud services. Lead projects, ensure production readiness and DevOps practices, integrate with Azure AI products, and collaborate with engineers, scientists, and stakeholders to deliver scalable, observable, and secure Responsible AI features and infrastructure.

Leads the architecture, development, and deployment of enterprise-scale AI systems and platforms. Establishes technical direction, governance, architecture standards, and responsible AI practices while modernizing legacy systems through code transformation and service decomposition. Develops AI-enabled SDLC practices, partners with engineering teams to drive adoption, and provides technical leadership and mentorship across software engineering, distributed systems, cloud, APIs, microservices, and DevOps.