Senior Director, AI Platform Engineering Management

ServiceNow•Santa Clara, CA

1d•$254,500 - $445,400

About The Position

ServiceNow is seeking a Senior Director of Engineering to lead multiple engineering teams and product areas across its AI Control Tower platform, which provides a central hub for governing enterprise AI models, agents, and datasets. Reporting to the VP of Engineering, AICT, this leader will help define the platform’s technical strategy, organizational structure, engineering culture, and execution model as the product and organization grow. The role calls for a startup-minded leader who has built products from zero to one, navigated early customer adoption and uncertainty, and scaled products, teams, and engineering organizations. The successful candidate will bring an AI-native approach, using and building with AI and understanding its effects on product architecture, engineering workflows, developer productivity, and enterprise customer expectations. The Senior Director will lead engineering strategy and execution across multiple product areas, provide architectural leadership, drive AI adoption throughout the engineering lifecycle, build and develop a high-performing organization, and collaborate with product, design, security, data, infrastructure, executive leaders, strategic customers, and field teams. The position is listed as a regular employee role with a flexible work persona. Compensation includes a base pay range of $254,500–$445,400, plus equity when applicable, variable or incentive compensation, and benefits; actual compensation depends on qualifications and work location.

Requirements

15+ years of software engineering experience, including significant experience leading large, complex engineering organizations and managing managers and senior technical leaders.
Experience operating at Director or Senior Director level, or with equivalent scope and accountability across multiple engineering teams or product areas.
Demonstrable startup or zero-to-one experience personally helping build, launch, and scale a new product or platform; ideally in an organization with fewer than 100 employees.
Experience taking an early-stage or 1.0 product through customer adoption and meaningful scale, navigating product-market uncertainty and changing direction based on customer behavior.
Deep technical foundation in cloud-native SaaS, including multi-tenant architectures, large-scale APIs such as REST and gRPC, Kubernetes, distributed systems, and multi-cloud platforms including AWS, Azure, and GCP.
Strong understanding of Infrastructure as Code and modern cloud operations, including technologies such as Terraform, CloudFormation, and Helm.
Strong understanding of database architecture, schema design, data modeling, and modern data platforms, including relational, graph, vector, columnar, OLAP, streaming, and event-driven architectures.
Experience designing scalable data architectures for AI, analytics, governance, and enterprise workloads.
Ability to define technical strategy and architecture across multiple teams while balancing speed, reliability, security, scalability, and long-term maintainability.
Experience applying AI across the engineering lifecycle, including product strategy, architecture, development, testing, security, deployment, observability, operations, and developer productivity.
Ability to work across executive strategy and organizational design as well as architecture and implementation details when required.
Track record of recruiting, developing, and retaining exceptional engineering leaders and technical talent.
Strong executive communication skills and ability to influence engineering, product, and business leaders across organizational boundaries.

Nice To Haves

Experience building governance, observability, compliance, or enterprise platform products where auditability, security, and explainability are core product requirements.
Experience building or operating AI platforms, agentic systems, or AI infrastructure in production.
Understanding of production AI agent operations, including tool reliability, context management, evaluation, observability, security, and failure modes.
Background in enterprise security, IAM, data governance, or privacy.
Familiarity with AI interoperability and integration standards such as MCP and OpenAPI.
Experience engaging directly with large enterprise customers while developing and scaling a new platform.
MS or PhD in Computer Science, AI, or a related technical field.

Responsibilities

Own engineering strategy across multiple AI Control Tower product areas, translating product vision into technical priorities, organizational investments, and execution plans.
Partner with VP-level Product and Engineering leaders to determine investment priorities, simplifications, and the platform’s evolution as customer adoption grows.
Lead products from initial creation through enterprise adoption and scale, establishing feedback loops among customers, Product, Engineering, and Design.
Provide technical leadership across architecture, platform strategy, data architecture, APIs, reliability, security, and operational scale.
Establish architectural principles that enable multiple teams to move quickly while avoiding platform fragmentation and technical debt; review critical architectural decisions and engage deeply when needed.
Set strategy and expectations for AI use across engineering, including product definition, architecture, implementation, code review, debugging, testing, security, CI/CD, observability, operations, and documentation.
Drive measurable improvements in engineering productivity, software quality, reliability, customer experience, and delivery velocity through AI adoption.
Build and scale a high-performing engineering organization by hiring and developing managers, architects, technical leaders, and engineers; build succession, raise technical standards, and foster accountability, experimentation, customer focus, and continuous learning.
Own execution across multiple teams and interconnected initiatives, establishing prioritization, dependency management, technical decision-making, and delivery mechanisms.
Partner with Product Management, Design, Security, Data, Infrastructure, and other engineering organizations; represent AICT Engineering in executive planning, architecture, investment, and product reviews.
Communicate technical and organizational decisions clearly to senior leaders and influence priorities across organizational boundaries.
Engage with strategic customers and field teams to understand enterprise AI deployment and governance needs, translating customer problems and feedback into platform capabilities and strategy.