Top AI Automation Jobs in Seattle, WA

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 the business-side AI Operations function to translate enterprise AI capabilities into operational implementation, adoption, training, hypercare, and sustainment. Build operating models, readiness frameworks, change plans, KPI tracking, and team capabilities to scale AI across the organization and ensure initiatives move from pilots to measurable business outcomes.

Build end-to-end internal AI agents and workflow automations: discover high-value opportunities, redesign processes, implement LLM-based agents with integrations, deploy and operate solutions, and ensure security, compliance, monitoring, and adoption across GTM teams.

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.

Contribute to building and hardening micro-apps and AI integrations on a shared GCP platform. Complete bug fixes and small features, participate in code and design reviews, apply security and software development best practices, monitor applications and follow runbooks, and use AI tools to assist development, testing, and documentation while learning from senior engineers.

Build and deploy production micro-apps and LLM-powered agents on a governed GCP platform. Integrate AI with systems (Salesforce, Slack, NetSuite, SQL), harden apps with auth, secrets, CI/CD, logging and monitoring, automate maintenance, and implement evaluation and observability for model quality and drift.

Lead the business-side AI Operations function to translate enterprise AI into adopted, measurable, and sustainable business practices. Build operating models, readiness frameworks, launch and hypercare plans, sustainment processes, and team capabilities. Partner with IT, AI Innovation, product, and business stakeholders to drive adoption, change management, KPI tracking, and continuous improvement across the organization.

Design, build, and productionize machine learning models to attribute cloud costs, forecast usage, and detect anomalies. Analyze large-scale cloud and financial datasets, run experiments, collaborate with engineers to deploy and monitor models, and report project status while improving team technical capability.

Design and deliver LLM-powered, agentic AI systems to automate customer support and compliance. Build scalable, secure backend infrastructure in Python and Go, integrate third-party models/APIs, implement grounding and guardrails, and measure performance. Lead end-to-end projects, mentor engineers, conduct code reviews, and participate in on-call and incident response to improve resolution times and reduce operational costs.

Lead intake, roadmap, and governance for an AI automation/platform team. Harden and ship business-built AI apps to production with secure GCP platforms, define acceptance criteria and responsible-AI gates, manage vendor buy-vs-build decisions, drive adoption, measure outcomes, and manage AI economics and stakeholder communication.

Support AI project teams by executing workstreams, preparing client-facing presentations and documentation, assisting implementation activities, maintaining required certifications, using relevant technology, communicating with stakeholders, and traveling as needed for client engagements.

Lead Net at Work’s transformation into an AI-native company by redesigning workflows, deploying virtual workers, building AI capabilities, training employees, and managing adoption. Establish the internal AI tool stack, standards, and use-case pipeline while delivering measurable operating leverage through reduced cycle times, improved quality, increased capacity, and margin gains. Validate results with Finance and drive organization-wide change through influence, process redesign, and strong stakeholder leadership.

Leads and grows a client-facing AI and data services practice, owning strategy, offerings, delivery methods, team development, client outcomes, sales support, and P&L performance. Guides clients from data readiness and AI use-case discovery through production deployment, adoption, and measurable business results. Advises executives, scopes complex engagements, builds delivery teams, and develops managed, subscription, and advisory services.

Lead client engagements in insurance data, analytics, and AI: define roadmaps, run discovery sessions, design solutions, manage delivery and teams, develop proposals, and ensure DataOps, cloud (AWS), governance, and best practices across AI and analytics initiatives.

Enable and mentor partners to sell and deliver NiCE AI/CCaaS solutions. Lead technical discovery, demos, PoVs, and architecture reviews; build partner enablement content; manage technical partner relationships, integrations, release impacts, and service scoping.

The Principal Consultant leads AI and digital transformation engagements, guides clients in identifying AI opportunities, builds strategies and roadmaps, implements solutions, and develops teams to achieve measurable business outcomes.

The role leads AI adoption across business functions, prioritizing use cases, building business cases, and driving organizational change necessary for successful implementation. It requires collaboration with IT leadership and the presentation of ROI to business stakeholders.

Own and administer Salesforce and NetSuite while designing and deploying production-grade AI-driven automations and agent workflows. Ensure data governance, PHI/HIPAA compliance, release management, and quote-to-cash integrity. Partner cross-functionally to prioritize work, coach teams, document systems, and measure impact of automation on operations and revenue.

Build and ship AI-native systems for a fintech app: AI agents, RAG/retrieval systems, tool-calling and automation for onboarding, KYC, support, risk, CRM and operations. Work with product and engineering to design reliable, low-hallucination, cost- and latency-aware solutions and turn manual processes into scalable AI-enabled systems.

Leads AI enablement and delivery for Finance by identifying use cases, training employees, and designing, building, and deploying AI-enabled workflows integrated with finance and ERP systems. Owns finance data readiness, governance, controls, audit compliance, and measurable automation outcomes across close, FP&A, revenue, audit, and procurement. Coordinates with Technology, Data, and enterprise AI stakeholders.

Develop AI capabilities using Microsoft tools, integrate AI solutions within enterprise systems, ensure compliance and security, and collaborate with team members for continuous improvement.

Lead data science strategy and a rapid-prototyping team to build production-grade ML and GenAI solutions for contractor earnings, tax, and compliance. Drive model governance, automation, and cross-functional delivery to reduce errors, mitigate risk, and scale measurable ROI across product, engineering, and operations.

The SME will develop a curriculum on AI for finance, creating engaging content and videos to train finance professionals in AI applications. They must leverage their extensive finance experience and AI understanding to enhance learner success.

Build and deploy AI-powered business workflows across B2B SaaS companies. Responsibilities include transforming business processes, implementing automation and enterprise systems, creating dashboards and KPI reports, integrating APIs, supporting UAT, improving data quality, documenting procedures, and driving user adoption across Sales, Marketing, Support, HR, Finance, and Operations.

Build and maintain internal and customer-facing web/cloud apps and AI automations. Integrate with third-party APIs (QuickBooks, Microsoft Graph, Zoho, Zoom), implement generative-AI features, ensure security, deploy to cloud, and collaborate with leadership and IT. Work independently on design, testing, and maintenance across varied projects.