Director, Software Engineering Management

About the team:

The CRM & Industry Workflows (CRM&I) engineering organisation builds the products that make ServiceNow's CRM vision real: Workplace Service Delivery, Customer Service Management, Field Service Management, Sales & Order Management, and the shared CRM Foundation that underpins them all. Our teams operate from IDC, AMS, and EMEA, and our work spans the full spectrum - from foundational platform capabilities to the agentic experiences that define how enterprises serve their employees and customers.

We are in the middle of a fundamental shift. AI is not a layer we are adding on top - it is the new baseline. We are rewriting how products are built, how engineers work, and what "done" means. If you are energised by that shift and want to lead teams that are living it, read on.

The role:

This Director of Software Engineering leads the Workplace engineering pillar within CRM&I. You will own Workplace Service Delivery and its adjacent integrations with CSM and FSM - building the employee and customer facing experiences that handle millions of service interactions across our largest enterprise customers.

What makes this role distinct from a traditional engineering director role is the AI-native mandate. You will not manage teams that use AI as a productivity tool - you will build and lead an organisation that operates as an AI-native engineering system. Your engineers work more like architects and verifiers than individual contributors typing every line: they decompose problems into agent-sized tasks, curate the context that makes agent output reliable, and own the verification harnesses that ensure correctness at scale.

You will report to the Senior Director, CRM&I Engineering and work closely with Workplace product management, platform engineering, and senior stakeholders to shape and deliver the Workplace roadmap for the next three to five years.

What you get to do:

• Build and lead two to three engineering managers who coach their teams to operate with AI-native practices as the default - not the exception.
• Model and scale the shift from implementation-speed metrics to judgment metrics: specification quality, architectural soundness, and verification rigour.
• Cultivate a culture where engineers own the correctness of output whether a human or an agent produced it, and are accountable for autonomous decision quality in production.
• Partner with product management and UX to define, own, and execute the Workplace Service Delivery roadmap - including employee self-service, request management, and WSD x CSM/FSM integration.
• Translate ambitious product goals and ambiguous problem statements into precise, testable specifications that both engineers and AI agents can act on with high accuracy.
• Make sound architectural and sequencing decisions early, before agent workstreams begin, and course-correct quickly when output diverges from intent.
• Design the verification and guardrail harnesses - automated tests, evaluation suites, CI/CD quality gates, and least-privilege execution environments - that make agent-generated output trustworthy at scale.
• Oversee context engineering across your teams: instruction files, architectural decision records, golden examples, and agent-readable documentation that drive reliable results from AI coding agents.
• Own AI reliability in production: monitor for hallucinations, behavioural drift, and safety regressions; maintain agent observability; and implement rollback mechanisms when autonomous behaviour deviates from intent.
• Set and hold the standard for enterprise-grade software quality - accessible, progressive, and responsive web and mobile experiences at the scale our customers demand.
• Champion security-minded engineering practices specific to AI-integrated systems: prompt injection defence, secret and data leakage controls, and tool-access governance.
• Treat the development process itself as a product: trace failures back to missing specs or context gaps, refine evaluation harnesses, and raise team-wide throughput and reliability.
• Mentor and grow engineering managers and senior engineers, investing in their technical depth, leadership range, and AI-native fluency.
• Create a talent environment where engineers who are energised by the AI-native shift can build careers through some of the most advanced systems in enterprise software.

To be successful in this role:

Experience:

• 15+ years of experience designing and shipping scalable enterprise software products, with significant time in technical leadership roles.
• Demonstrated track record of building and leading engineering teams that have delivered real production systems using AI-native methods - agentic coding tools, AI-assisted workflows, and autonomous agent architectures.
• Experience in CRM, ITSM, workplace service delivery, or related enterprise domains preferred.

AI-native engineering fluency:

• Agentic AI and autonomous systems. Hands-on experience designing and operating autonomous agent systems - planning loops, dynamic tool invocation, memory management, execution policies, and multi-agent collaboration - that select and sequence actions reliably in production.
• Context engineering. Skill in designing what information models receive, when it is retrieved, and how context evolves. Includes assembling instruction files, documentation, examples, and architectural constraints, as well as architecting retrieval systems, vector databases, and long-term memory frameworks.
• Verification and harness design. Experience building the guardrail systems that make agent output reliable at scale: property-based and mutation tests, evaluation suites, CI/CD quality gates, and provenance and permission controls.
• Outcome evaluation. Ability to develop measurable frameworks - benchmarks, golden datasets, continuous evaluation pipelines, and drift detection - that hold production AI to the same rigour as deterministically testable software.
• Security and reliability mindset. Working knowledge of risks specific to AI-integrated systems: prompt injection, sensitive-data leakage, tool-access governance, and model abuse, translated into concrete engineering controls.

Leadership and collaboration:

• Strong written and verbal communication - able to operate as a credible technical voice with product, platform, and executive stakeholders.
• Ability to manage multiple competing priorities and make clear decisions under uncertainty without waiting for perfect information.
• Conviction to influence product vision and roadmap, paired with the humility to listen, learn, and adapt.

Forward deployed engineering:

• Customer-embedded delivery. Experience leading or participating in forward deployed engineering (FDE) engagements - working on-site or in close collaboration with strategic customers to accelerate time-to-value, co-develop solutions, and unblock adoption of product capabilities.
• Translating customer context into product decisions. Ability to synthesise what you learn at the customer boundary - pain points, integration constraints, workflow gaps, and adoption blockers - into precise product requirements and engineering priorities that feed back into the core roadmap.
• Executive and technical credibility with customers. Comfortable presenting architecture decisions, roadmap trade-offs, and delivery progress to both customer engineering leads and C-suite stakeholders, earning trust through technical depth and clear communication rather than sales framing.
• Rapid scoping and delivery under constraints. Skilled at decomposing ambiguous customer problems into tightly scoped engineering workstreams with clear success criteria, then driving rapid iteration cycles - days to weeks, not quarters - while managing risk and communication on both sides.
• Cross-functional coordination in a customer environment. Experience coordinating across product, sales, and professional services teams while embedded with a customer - navigating competing priorities, escalation paths, and success handoffs without losing engineering focus.

Technical depth:

• Strong command of data structures, algorithms, system design, and modern software development practices. Good to have technologies like HTML, CSS, JavaScript and Java.
• Solid data modelling background - relational and otherwise - with experience building systems where data model quality is a first-class concern.
• Proficiency with AI/ML fundamentals: model training and evaluation, embeddings, and the probabilistic failure modes of LLMs, sufficient to reason about and debug model-driven behaviour in production.

Even better if you have:

• Experience with the ServiceNow platform - scoped application architecture, Glide APIs, or platform engineering at scale.
• Hands-on exposure to multi-tenant SaaS architecture and the operational realities of shipping to thousands of enterprise customers simultaneously.
• Experience building and operating agentic systems in a regulated or enterprise-compliance context.
• Prior experience managing distributed engineering organisations across time zones.
• Direct experience leading forward deployed engineering engagements - co-developing solutions alongside strategic customers, accelerating adoption of complex capabilities, and feeding field learnings back into the product roadmap.

Skills

AI, Agentic AI, Algorithms, Architecture, CSS, Data Structures, HTML, Java, JavaScript, LLM, Machine Learning, System Design