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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Head of AI Deployment & Community - **Company:** Franklin Resources,Inc. - **Location:** San Ramon, CA, United States - **Experience:** Expert - **Salary:** $208,600 - $245,500 - **Contract:** Permanent contract - **Skills:** Reference Implementation, Artificial Intelligence, Data Security, Open Source Technology, Software Product Management, Software Deployment, Management of Software Versions, Model Validation, AI Platforms - **Published:** November 9, 2026 - **Apply:** https://www.careerbuilder.com/job-details/head-of-ai-deployment-community-san-ramon-ca--c887117d-8cd5-4352-b562-64f10cd3ca3e ## About the Role Artificial Intelligence (AI), Customer Relations, Asset Management, Product Lifecycle, Product Development, Artificial Intelligence (AI), Problem Solving Skills, Product Engineering, Interpersonal Skills, Workflow Analysis, * Senior experience in a combination of forward deployed engineering, applied AI, AI platform engineering, technical product leadership, enterprise architecture, developer experience, InnerSource or open-source program leadership, or technical transformation. * Demonstrated ability to work hands-on with engineering teams in ambiguous environments and convert messy problems into working systems, credible technical paths, or durable operating practices. * Strong understanding of modern AI application patterns, including agents, tool use, retrieval, evaluation, prompt and skill versioning, observability, workflow automation, and production risk controls. * Experience designing or operating product development lifecycles, engineering operating models, platform capabilities, developer platforms, or self-service systems. * Experience building adoption around a shared technical platform or practice, including contribution workflows, documentation, governance, incentives, and community operations. * Ability to communicate credibly with senior executives and deeply technical practitioners, and to influence without relying solely on formal authority. * Track record of earning trust across teams, sharing credit generously, and leaving teams more capable than before the engagement. ## Description We are looking for a senior leader to build the firm's AI deployment and community capability. This person will lead forward deployed AI practitioners who embed with product and engineering teams, help those teams solve consequential problems, and turn field learning into a practical AI product development lifecycle. At the same time, they will build the internal community, reusable-asset platform, contribution model, and incentives that allow successful work to spread across the organization. The role connects delivery and leverage. It ensures that every deployment creates a local outcome and a reusable organizational asset: an agent skill, evaluation harness, workflow pattern, reference implementation, playbook, or lesson that another team can discover, trust, adapt, and improve. The ideal candidate combines applied AI and platform depth, product and operating-model judgment, community-building instincts, and the interpersonal credibility to earn trust with teams rather than feel imposed on them. How you will add value Forward Deployment and AI Product Development Lifecycle (45 Percent) * Build and lead a forward deployed AI practitioner capability that embeds with product and engineering teams to solve real problems while transferring practical AI skills. * Select, prioritize, and staff deployments based on business value, learning potential, technical feasibility, and the opportunity to create reusable enterprise capability. * Personally lead the most complex, visible, or strategically important engagements, including ambiguous AI opportunities, stalled initiatives, and cross-functional operating-model challenges. * Partner with engineering, product, architecture, security, data, risk, and business leaders to identify high-leverage AI opportunities and convert them into executable engagements. * Create and continuously improve an AI-native product development lifecycle that covers discovery, prototyping, data access, agent and model evaluation, human review, risk controls, release, observability, feedback loops, and post-release improvement. * Community Infrastructure and Management (25 Percent) * Specify and evolve the internal AI community and InnerSource mechanism for sharing software components, agent skills, prompts, workflows, evaluation assets, documentation, and reference implementations. * Build a trusted, searchable repository with clear ownership, documentation, quality tiers, contribution workflows, maintenance expectations, and signals that help teams distinguish experimental from supported assets. * Create community momentum through working groups, office hours, showcases, contributor onboarding, recognition, mentorship, and practical demonstrations of reuse value. * Design lightweight governance that enables safe and maintainable reuse without slowing experimentation or turning the platform into a bureaucratic control point. * Work with leaders and HR partners to make contribution, maintenance, mentorship, and reuse visible forms of technical impact. Shared Responsibilities (30 percent) * Translate deployment learnings into reusable patterns, reference architectures, templates, evaluation methods, technical standards, and practical playbooks. * Set the integrated roadmap that links deployment priorities, product development lifecycle improvements, platform investment, and community growth. * Measure deployment outcomes, reuse, adoption, contributor health, asset quality, capability growth, time saved, and duplication avoided; use the evidence to improve both mandates. * Provide executive visibility into deployment outcomes, reuse, adoption, risks, constraints, capability growth, and the investment required to scale the practice. * Ensure that teams retain ownership of their systems while benefiting from shared assets, collective learning, and a consistent path from experimentation to responsible production use., * Equal comfort discussing code, architecture, product strategy, operating models, adoption, and executive-level tradeoffs. * A pragmatic systems thinker who reduces friction and coordination cost rather than adding methodology for its own sake. * A community builder who makes contribution and reuse feel prestigious, useful, and safe. * Strong product instincts for discoverability, onboarding, documentation, trust signals, sandboxing, and developer experience. * The judgment to know when to standardize, when to preserve local autonomy, and when an exception is the right answer. * A bias toward measurable outcomes, durable capability-building, and visible wins over one-off demonstrations. Measures of Success * Embedded teams move high-value AI opportunities from ambiguous ideas to production outcomes or well-supported stop decisions faster and with less coordination burden. * Teams report increased AI fluency, delivery confidence, and ownership after deployments. * Patterns from deployments become reusable assets that other teams can find, trust, adopt, and improve. * The organization increasingly reuses AI components and methods instead of rebuilding them independently. * Community participation grows through contributors, maintainers, mentors, and champions across business units. * Lightweight governance improves confidence, quality, and maintainability without becoming a bottleneck. * Senior stakeholders view the function as a partner that accelerates responsible delivery and creates enterprise leverage.