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Director Ai Automation Jobs in Renton, WA (NOW HIRING)

Director, AI Product Management

Seattle, WA ยท On-site

$150 - $200/hr

About the Position WatchGuard is looking for a Director of AI Product Management to own the ... Define and track success metrics for Rai features: automation rate, ticket deflection, time-to ...

Director, Applied AI

Seattle, WA ยท On-site

$225K - $265K/yr

Who you are Metropolis is seeking a Director of Applied AI to lead our newly formed Applied AI ... Maintain deep hands-on familiarity with LLMs, agentic workflows, and AI automation tooling such as ...

Who you are Metropolis is seeking a Director of Applied AI to lead our newly formed Applied AI ... Maintain deep hands-on familiarity with LLMs, agentic workflows, and AI automation tooling such as ...

Director, Applied AI

Seattle, WA ยท On-site

$225K - $265K/yr

Who you are Metropolis is seeking a Director of Applied AI to lead our newly formed Applied AI ... Maintain deep hands-on familiarity with LLMs, agentic workflows, and AI automation tooling such as ...

Our goal is to become the go-to AI platform for all enterprise automation, powered by our voice ... About the Role We're looking for a Sales Director to own and expand relationships with Giga ...

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Director Ai Automation information

See Renton, WA salary details

$34.9K

$131.2K

$190.7K

How much do director ai automation jobs pay per year?

As of Jul 26, 2026, the average yearly pay for director ai automation in Renton, WA is $131,163.00, according to ZipRecruiter salary data. Most workers in this role earn between $102,900.00 and $156,400.00 per year, depending on experience, location, and employer.

What is the difference between Director Ai Automation vs Data Scientist?

AspectDirector Ai AutomationData Scientist
Required CredentialsBachelor's or Master's in Computer Science, Engineering, or related field; experience in AI and automation projectsBachelor's or Master's in Data Science, Statistics, Computer Science; proficiency in programming and statistical analysis
Work EnvironmentLeads AI automation teams, collaborates with engineering and product teams, oversees project implementationAnalyzes data, develops models, and provides insights; often works in research or analytics teams
Employer & Industry UsageTech companies, manufacturing, finance, and industries adopting AI automationTech firms, research institutions, finance, healthcare, and analytics-focused organizations

While both roles require technical expertise in AI and data handling, the Director Ai Automation focuses on leading automation projects and managing teams, whereas Data Scientists primarily analyze data and develop models. The Director role is more strategic and managerial, while Data Scientists are more hands-on with data analysis and model development.

What are the key skills and qualifications needed to thrive as a Director of AI Automation, and why are they important?

To thrive as a Director of AI Automation, you need deep expertise in artificial intelligence, machine learning, process automation, and strategic leadership, often supported by an advanced degree in computer science or engineering. Familiarity with AI development frameworks, cloud platforms, data analytics tools, and certifications like PMP or Six Sigma are commonly required. Outstanding communication, visionary problem-solving, and team leadership skills set top performers apart in this role. These skills ensure the successful implementation of AI automation strategies that drive efficiency, innovation, and business growth.

What are some common challenges faced by a Director of AI Automation when implementing new automation strategies within an organization?

A Director of AI Automation often encounters challenges such as integrating new AI solutions with existing legacy systems, managing change resistance among staff, and ensuring that automation initiatives align with business goals. Balancing the need for rapid innovation with data privacy and compliance requirements is also critical. Additionally, fostering effective collaboration between data scientists, engineers, and business stakeholders is essential to ensure successful deployment and adoption of AI-driven automation.

What does a Director of AI Automation do?

A Director of AI Automation leads the strategy, development, and implementation of artificial intelligence and automation solutions within an organization. They oversee teams working on automating business processes, integrating AI technologies, and optimizing workflows to improve efficiency and reduce costs. This role involves collaborating with various departments to identify automation opportunities, ensuring compliance with regulations, and staying updated on emerging AI trends. The Director also manages budgets, mentors staff, and measures the impact of automation initiatives on business outcomes.
What are the most commonly searched types of Ai Automation jobs in Renton, WA? The most popular types of Ai Automation jobs in Renton, WA are:
What job categories do people searching Director Ai Automation jobs in Renton, WA look for? The top searched job categories for Director Ai Automation jobs in Renton, WA are:
What cities near Renton, WA are hiring for Director Ai Automation jobs? Cities near Renton, WA with the most Director Ai Automation job openings:

Director, AI Product Management

Jobtailor

Seattle, WA โ€ข On-site

$150 - $200/hr

Other

Posted 13 days ago


Job description

About the Position

WatchGuard is looking for a Director of AI Product Management to own the strategy and execution for Rai, our agentic, AI-powered action layer built on WatchGuard Cloud, WatchGuardโ€™s MSP focused security platform. This is a high-impact role at the intersection of agentic AI, cybersecurity, and the managed services market.

Rai handles the MSP workforce jobs that are structured, repeatable, and grounded in data that already lives in WatchGuard Cloud. It doesnโ€™t just surface information; it closes loops. This PM owns what those loops look like, how reliable they are, and how MSPs learn to trust and extend them.

Reporting to the Chief Product Officer, this individual will be responsible for driving the Rai product roadmap, working cross-functionally with engineering, design, channel, and go-to-market teams, and ensuring that WatchGuardโ€™s AI platform delivers measurable value to MSP partners.

A Day in the Life

As a Director of AI on the Platform team, you will work at the center of one of WatchGuardโ€™s most strategically important investments. You will spend your time in direct conversation with MSP partners, understanding how they staff their SOCs and NOCs, where their technicians lose time, and what it would mean to their business to have those hours back. You will translate that understanding into agentic workflows that Rai can own autonomously, and work with engineering to define how those workflows behave when data is incomplete, confidence is low, or actions cannot be undone. AI tools are a native part of how you work; you use them to synthesize customer research, accelerate discovery, apply Spec Driven Design principles to structure requirements before engineering picks them up, and validate prototypes faster than traditional methods allow. You evaluate AI feature quality not just by adoption but by accuracy, reliability, and the degree to which MSPs choose to expand Raiโ€™s scope over time. You drive roadmap alignment across a cross-functional team using working prototypes and real partner feedback, and you partner with PMM and the channel to ensure that what gets built also gets understood and sold.

Position Responsibilities
  • Business ownership: Ensure the agents are monetizable and a commercial success. You will drive the ideation, design, and development of AI-powered agents and solutions, with a focus on creating monetizable agentic capabilities aligned to MSP market needs.
  • Roadmap ownership: Own the Rai product roadmap from discovery through delivery, balancing near-term partner value with the longer-term platform convergence vision.
  • Agentic workflow definition: Define and prioritize agentic workflows that move MSPs from visibility to decision to action, replacing or extending MSP workforce jobs rather than just adding a chat interface.
  • AI evaluation and quality: Establish evaluation frameworks for AI features, including how WatchGuard defines quality bars, measures accuracy and reliability, and decides when an automated action is ready for production.
  • Customer discovery: Work directly with MSP partners to understand workflows and pain points, and validate product direction through direct customer engagement.
  • Cross-functional alignment: Drive alignment across engineering, channel, PMM, and leadership using working prototypes and real partner feedback. Clearly conveying the outcomes to each stakeholder.
  • AI safety and reliability: Own AI safety and reliability as a product responsibility, including how Rai behaves when confidence is low, when actions are irreversible, and when the MSPโ€™s trust is on the line.
  • Go-to-market partnership: Partner with PMM, channel, and sales to translate product capability into GTM strategy, partner messaging, and enablement.
  • Performance monitoring: Define and track success metrics for Rai features: automation rate, ticket deflection, time-to-action, accuracy, and downstream business outcomes for MSPs.
  • Competitive intelligence: Monitor the AI and MSP platform competitive landscape to identify differentiation opportunities.
Required Qualifications
  • MSP market knowledge: Deep familiarity with how managed service providers operate, including how they structure their teams, price and deliver services, manage margin pressures, and where technician time goes. You understand that for MSPs, simplicity and automation are not features; they are the business case. You know the difference between a tool an MSP will actually adopt and one that adds process to an already stretched team.
  • Agentic AI product experience: Demonstrated depth in product management, with meaningful hands-on experience shipping agentic AI or LLM-powered automation in a B2B context โ€” typically 8+ years overall and at least 2 years working directly on autonomous or semi-autonomous AI workflows where the system takes action on behalf of the user. Candidates who have shipped real agentic products recently will be weighted over those with tenure alone.
  • LLM technical fluency: Hands-on familiarity with how LLMs work in production: context limits, latency tradeoffs, hallucination risks, and when RAG, fine-tuning, or deterministic fallbacks are the right answer.
  • AI evaluation and governance: Experience defining evaluation criteria and quality standards for AI actions, including how to validate that an automated workflow is safe to run unsupervised.
  • AI-native product approach: AI tools are part of your core workflow. You use them for customer research synthesis, Spec Driven Design, and prototype validation, getting to a well-structured spec faster and with more rigor than traditional methods allow. You think in terms of what AI can own end-to-end, not just where it can assist.
  • Product instincts: Strong instincts for what makes an agentic feature genuinely useful versus impressive in a demo, especially in an MSP context where trust, reliability, and low-friction adoption determine whether a product survives the first 90 days.
  • Cross-functional collaboration: Comfort working across engineering, design, and go-to-market in a fast-moving environment.
  • Communication skills: Clear, direct communicator who can move between technical depth and business narrative depending on the audience.
Nice to Have
  • PSA and RMM familiarity: Experience with MSP operational tooling including ConnectWise, Autotask, NinjaOne, or HaloPSA.
  • Security or platform background: Background in cybersecurity products, managed services, or multi-tenant SaaS platforms.
  • Prototyping experience: Experience building or evaluating functional prototypes as a discovery and alignment tool.
  • Responsible AI: Understanding of responsible AI in production environments, including explainability, auditability, rollback behavior, and least-privilege action design.
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