Dir, Software Engrg Mgmt

Job Description

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

Lead and develop an AI-native engineering organisation

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.

Drive the Workplace product roadmap

Partner with product management and UX to define, own, and execute the Workplace Service Delivery roadmap — including employee self-service, request management, and WSD×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.

Build reliable AI-native systems

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.

Raise the bar on quality, security, and delivery

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.

Develop the next generation of engineering leaders

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.