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Director

KPMG Global Services
Bengaluru, IND
Full-time
Director · 15+ years experience
Onsite
Discovered 2 weeks ago

Full Job Posting

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.

Qualifications

  • 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.

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