"Seeking a hands-on Engineering Leader and AI evangelist who has led large product engineering organizations and can drive enterprise-wide AI-augmented SDLC transformation, rather than a traditional architect, delivery manager, or services leader."

A pure-play Engineering Leader, rather than an Enterprise Architect or conventional delivery manager.

The candidate should be sufficiently hands-on to understand engineering execution and drive teams, while also demonstrating transformation of engineering/SDLC delivery, ideally with measurable evidence of reducing time-to-market.

Key attributes

- Full-stack / broad engineering orientation, rather than specialization in one technology stack.
- Evidence of SDLC transformation and delivery acceleration, including examples of compressing development cycles/time-to-market.
- AI exposure can be relevant in the context of transforming delivery, but the team does not want a niche AI systems profile.
- Candidates should have worked across multiple industries/domains rather than spending most of their careers in one vertical.
- Enterprise Architect profiles should not be considered. The requirement is specifically for an engineering leader who is closer to execution.
- Leadership scope, overall experience, team management and hands-on engineering credibility should form part of the pre-screen.

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.

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.

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.

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 Capability-building and change leadership: Able to design role-based learning, establish fluency pathways, mobilize champion networks and embed recent 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.·

📌 Director : Engineering Lead - Transformation (Bengaluru)
🏢 BIG4
📍 Bengaluru