Senior Manager, AI Corporate Engineering

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Manager, AI Corporate Engineering based in the United States.

This is a senior engineering leadership role responsible for building the internal AI platform layer that enables a growing organization to adopt AI safely and effectively. You will lead a small, experienced team spanning platform engineering and technical program management while defining the function’s charter, roadmap, and operating model. The scope covers AI access and identity, cost engineering, MCP infrastructure, internal application platforms, governance, and technical enablement. You will establish the guardrails, automation, and self-service capabilities needed to accelerate AI adoption without increasing unnecessary risk, cost, or duplication. The role requires strong technical depth combined with the ability to influence Engineering, Security, GRC, Data, Finance, and People teams. You will operate in a fast-moving environment where many AI capabilities are already in use and where the next challenge is creating scalable infrastructure and governance around them. This is an opportunity to shape an emerging discipline from the ground up while helping a distributed workforce use AI with greater speed, safety, and impact.

Accountabilities:: Lead the AI Corporate Engineering team: Manage, coach, and develop a senior team across platform engineering and technical program management, setting clear priorities, maintaining high standards, and encouraging data-driven challenge and decision-making.

Define the function's charter: Establish what the team owns, what it enables, how work enters the organization, and how cross-functional AI initiatives remain coordinated rather than duplicated.

Own the AI access platform: Design and manage identity and authentication for AI tools, connector and integration allowlisting, permissions by function, and security guardrails that make responsible adoption safe by default.

Drive AI cost engineering: Build usage attribution at team and individual levels, optimize model selection, make agent and automation costs traceable, and establish budget thresholds and alerts in partnership with Finance.

Manage MCP infrastructure: Own the internal MCP layer, including commissioning, decommissioning, governance, and impact assessments before migrations or changes are deployed.

Build the internal application platform: Establish a reliable golden-path deployment model with sensible defaults, including private-by-default configurations, lifecycle management, automated decommissioning, and clear build-versus-buy principles.

Develop AI enablement capabilities: Create skills and agent registries, establish evaluation frameworks for internal AI usage, and provide technical enablement that helps employees use sanctioned AI tools effectively rather than simply granting access.

Scale self-service adoption: Design systems and processes that allow employees across technical and non-technical functions to safely build and use internal AI capabilities without compromising security standards.

Partner cross-functionally: Work closely with Engineering, GRC, Security, Data, Finance, and People teams to coordinate policies, technical infrastructure, budgets, and organizational requirements.

Manage scope and execution: Turn broad or previously unscoped commitments into practical, bounded deliverables, establish priorities, and make disciplined decisions about what should and should not be built.

Provide technical leadership: Review architecture and implementation decisions across LLM platforms, agents, MCP, identity, OAuth, CI/CD, and infrastructure as code, distinguishing material risks from less critical concerns.

Strengthen governance: Establish practical data governance standards for prompts, repositories, and third-party AI tools while enabling teams to move quickly without creating unnecessary bottlenecks.

Requirements

Platform leadership: 8+ years of experience in platform, infrastructure, systems engineering, or related technical fields, including at least 3 years managing and developing engineers.

Internal platform creation: Demonstrated experience establishing an internal AI, developer, or infrastructure platform function from an early stage, including defining its charter, intake model, roadmap, and operating practices.

AI cost engineering: Strong understanding of usage attribution, model right-sizing, token economics, agent cost traceability, budget controls, and the distinction between hard limits and effective guardrails.

Technical depth: Hands-on knowledge of LLM platform tooling, agent and MCP architecture, identity and OAuth, CI/CD, infrastructure as code, and related platform technologies.

AI governance: Strong understanding of data governance and responsible AI practices, including the ability to identify information that should not enter prompts, repositories, or third-party tools.

Enablement mindset: A strong preference for safe self-service and practical enablement, with the ability to provide alternative solutions when access or functionality cannot be approved.

Organizational leadership: Experience building teams, establishing operating models, setting standards, and scaling engineering capabilities through periods of significant organizational growth.

Influence without authority: Comfortable driving initiatives across organizations where product AI, security policy, data platforms, and budget authority may sit with separate teams.

Business judgment: Ability to balance technical quality, security, cost, speed, and organizational needs while making pragmatic decisions in ambiguous environments.

Communication: Strong ability to explain complex technical concepts to engineers, executives, and increasingly non-technical internal users.

AI fluency: Curiosity and willingness to use AI to amplify personal and team capabilities, combined with sound judgment around responsible AI adoption.

Work authorization: Must be authorized to work in the United States without requiring current or future employer sponsorship.

Growth-stage experience: Experience working within an organization that has undergone significant headcount or operational growth is highly valuable.

Benefits

Compensation: Industry-competitive salary and equity.

Healthcare: Comprehensive medical, dental, and vision coverage, with 100% of employee-only premiums covered for most medical plans.

Parental leave: 16 weeks of paid parental leave for all new parents.

Health and wellness: Health and wellness stipend and additional employee well-being resources.

Remote work support: Stipends for remote workspace, internet, and cellphone expenses.

Commuter benefits: Commuter support for employees reporting to the San Francisco and New York City offices.

Family support: Family planning benefits.

Retirement: Matching 401(k) contributions with immediate vesting.

Time off: Flexible PTO policy, 80 hours of sick time, and 11 company-paid holidays.

Team culture: Virtual team-building activities, lunch-and-learns, and company-wide events.

Global offices: Access to offices in San Francisco, New York City, London, Dublin, Tel Aviv, and Sydney.

Remote flexibility: Remote position with the flexibility and infrastructure designed to support distributed employees.

Inclusive workplace: A collaborative environment committed to diverse perspectives, equal opportunity, and an inclusive employee experience.

Compensation transparency: U.S. base pay ranges are provided for transparency, with final offers determined by factors including location, skills, experience, and relevant credentials.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best!  Why Apply Through Jobgether?    Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.     #LI-CL1