You will be taken to KPMG’s own careers page to apply. Job ID 30050509.
Job description
Role Description Delivery Transformation Director will lead the enterprise-wide adoption and institutionalization of AI-augmented practices across the end-to-end Software Development Life Cycle (SDLC), from ideation, requirements and architecture through development, testing, deployment and ongoing operations. The role will be accountable for translating the expertise of early adopters into scalable, repeatable capabilities; establishing common SDLC standards, enablement, governance and measurement frameworks; developing a distributed network of champions across delivery pods; and fostering the incentives and safeguards required for responsible adoption. The Director will ensure these practices are consistently embedded and proliferated across the organization, delivering measurable improvements in engineering productivity, quality, speed to market, delivery effectiveness and commercial outcomes.
Key Responsibilities
Lead the Foundation-then-Embed operating model; establish, coach and orchestrate a pod-level champion network; and make the delivery pod, rather than the individual practitioner, the primary unit of adoption.
Codify proven practices into reusable patterns, prompt libraries and golden-path SDLC workflows; maintain alignment with evolving AI capabilities, enterprise standards and approved tooling.
Own the AI fluency ladder, role-based enablement and protected learning period; partner with People/HR to embed AI-augmented SDLC proficiency within career pathways and capability frameworks.
Own the measurement framework across the organization establish baselines, track adoption and outcomes, and safeguard the charter by ensuring metrics are used for development and improvement.
Partner with delivery leaders to strengthen test automation, batch discipline, engineering controls and review rigor in step with increased throughput, ensuring that speed does not compromise quality, security or maintainability
Translate productivity and delivery gains into cost-to-serve, margin and deal-competitiveness outcomes; own tool-clearance mapping and coordinate with Risk, Security and client teams to support responsible, compliant adoption.
Secure, sustain and protect the required leadership commitments; provide delivery leadership with transparent reporting on adoption, performance and realized outcomes against agreed baselines.
Ideal Candidate Profile The ideal candidate combines hands-on AI and engineering credibility with the executive influence, commercial judgment and change leadership required to embed AI-augmented delivery across the organization.
Hands-on AI-augmented delivery mastery: Demonstrated Multiplier-level proficiency across the end-to-end SDLC, with practical experience applying AI to requirements, architecture, development, testing, deployment and operations.
Delivery and engineering leadership at scale: Proven experience leading distributed, multidisciplinary engineering teams and complex delivery portfolios, with strong knowledge of DevSecOps, platform engineering, test automation, batch discipline, DORA and DX Core 4.
Capability-building and change leadership: Able to design role-based learning, establish fluency pathways, mobilize champion networks and embed new practices within delivery pods while addressing resistance and sustaining trust.
Commercial and professional-services fluency: Strong understanding of utilization, cost-to-serve, margin, and pricing, with the ability to quantify value and build a compelling commercial case.
Executive presence and organizational influence: Possesses the seniority, judgment and credibility to secure partner-level commitments, align stakeholders, protect program guardrails and sustain sponsorship.
Data and measurement literacy: Able to establish meaningful baselines, interpret engineering and developer-experience metrics, distinguish correlation from causation and prevent target gaming or misuse of team-level measures.
Governance, risk and responsible AI judgment: Experienced in navigating tool clearance, data protection, security, model risk and client constraints, creating pragmatic guardrails that enable responsible adoption.
Communication and executive storytelling: Communicates credibly with engineers, delivery leaders, commercial stakeholders and senior executives, translating technical evidence into clear decisions and outcome-based narratives.
Indicative experience: 15+ years of progressive experience across software engineering, technology-enabled delivery transformation and consulting or professional services, including leadership of enterprise-scale change and hands-on use of AI-enabled engineering tools in production environments.
Responsibilities
Lead the Foundation-then-Embed operating model; establish, coach and orchestrate a pod-level champion network; and make the delivery pod, rather than the individual practitioner, the primary unit of adoption.
Codify proven practices into reusable patterns, prompt libraries and golden-path SDLC workflows; maintain alignment with evolving AI capabilities, enterprise standards and approved tooling.
Own the AI fluency ladder, role-based enablement and protected learning period; partner with People/HR to embed AI-augmented SDLC proficiency within career pathways and capability frameworks.
Own the measurement framework across the organization establish baselines, track adoption and outcomes, and safeguard the charter by ensuring metrics are used for development and improvement.
Partner with delivery leaders to strengthen test automation, batch discipline, engineering controls and review rigor in step with increased throughput, ensuring that speed does not compromise quality, security or maintainability
Translate productivity and delivery gains into cost-to-serve, margin and deal-competitiveness outcomes; own tool-clearance mapping and coordinate with Risk, Security and client teams to support responsible, compliant adoption.
Secure, sustain and protect the required leadership commitments; provide delivery leadership with transparent reporting on adoption, performance and realized outcomes against agreed baselines.
Requirements
The ideal candidate combines hands-on AI and engineering credibility with the executive influence, commercial judgment and change leadership required to embed AI-augmented delivery across the organization.
Hands-on AI-augmented delivery mastery: Demonstrated Multiplier-level proficiency across the end-to-end SDLC, with practical experience applying AI to requirements, architecture, development, testing, deployment and operations.
Delivery and engineering leadership at scale: Proven experience leading distributed, multidisciplinary engineering teams and complex delivery portfolios, with strong knowledge of DevSecOps, platform engineering, test automation, batch discipline, DORA and DX Core 4.
Capability-building and change leadership: Able to design role-based learning, establish fluency pathways, mobilize champion networks and embed new practices within delivery pods while addressing resistance and sustaining trust.
Commercial and professional-services fluency: Strong understanding of utilization, cost-to-serve, margin, and pricing, with the ability to quantify value and build a compelling commercial case.
Executive presence and organizational influence: Possesses the seniority, judgment and credibility to secure partner-level commitments, align stakeholders, protect program guardrails and sustain sponsorship.
Data and measurement literacy: Able to establish meaningful baselines, interpret engineering and developer-experience metrics, distinguish correlation from causation and prevent target gaming or misuse of team-level measures.
Governance, risk and responsible AI judgment: Experienced in navigating tool clearance, data protection, security, model risk and client constraints, creating pragmatic guardrails that enable responsible adoption.
Communication and executive storytelling: Communicates credibly with engineers, delivery leaders, commercial stakeholders and senior executives, translating technical evidence into clear decisions and outcome-based narratives.
Indicative experience: 15+ years of progressive experience across software engineering, technology-enabled delivery transformation and consulting or professional services, including leadership of enterprise-scale change and hands-on use of AI-enabled engineering tools in production environments.
