Senior Director, AI Platform Engineering Management

ServiceNow•Santa Clara, CA

11h

About The Position

ServiceNow is seeking a Senior Director of Engineering to lead multiple engineering teams and product areas across its AI Control Tower (AICT) platform. The AI Control Tower is ServiceNow’s central hub for governing enterprise AI models, agents, and datasets. The role combines startup-style product building with the leadership needed to scale products, teams, and organizations, including taking products from initial creation through enterprise adoption. Reporting to the VP of Engineering, AICT, this leader will help define the platform’s technical strategy, organizational structure, engineering culture, and execution model. The role requires an AI-native leader who uses and builds with AI and understands its impact on product architecture, engineering workflows, developer productivity, and enterprise customer expectations. The Senior Director will provide technical and organizational leadership across architecture, platform and data strategy, APIs, reliability, security, and operational scale. They will build and develop engineering leaders and teams, establish effective prioritization and delivery mechanisms, represent AICT Engineering in executive reviews, and work closely with customers and cross-functional partners to shape the platform as enterprise adoption grows.

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 API design 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 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 move between executive strategy and organizational design and technical architecture or implementation details as needed.
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 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 and guide platform evolution as customer adoption accelerates.
Lead products from initial creation through enterprise adoption and scale.
Establish feedback loops among customers, Product, Engineering, and Design, using customer traction and behavior to guide product decisions.
Provide senior technical leadership across architecture, platform strategy, data architecture, APIs, reliability, security, and operational scale.
Establish architectural principles that enable teams to move quickly while avoiding platform fragmentation and technical debt.
Review critical architectural decisions, challenge assumptions, and engage with technical details when needed.
Set strategy and expectations for AI use across the engineering organization and drive its application throughout product definition, architecture, implementation, code review, debugging, testing, security, CI/CD, observability, operations, and documentation.
Create measurable improvements in engineering productivity, software quality, reliability, customer experience, and delivery velocity.
Build and scale a high-performing engineering organization by hiring and developing engineering managers, architects, technical leaders, and engineers.
Develop leadership succession, raise the technical bar, and foster accountability, experimentation, customer focus, and continuous learning.
Own execution across multiple teams and interconnected initiatives, establishing mechanisms for prioritization, dependency management, technical decisions, and delivery.
Partner with Product Management, Design, Security, Data, Infrastructure, and other engineering organizations.
Represent AICT Engineering in executive planning, architecture, investment, and product reviews; communicate decisions and influence priorities across organizational boundaries.
Engage strategic customers and field teams to understand enterprise AI deployment and governance needs, translating customer problems and feedback into platform capabilities and strategy.