AI SDLC Transformation Manager

We Are:   Strategic Technology Effectiveness, part of Accenture's Technology Strategy & Transformation Organization. We rebuild how large enterprises define, design and govern software, and we design and implement the agentic software and processes that run it. That work changes the operating model an engineering organization runs on, the ways of working underneath it, and the tooling that makes both of those real.   We start with evidence. Cycle time, flow efficiency, WIP and throughput show where delivery stalls, and that diagnosis sets the target. What we design from it is AI-native by construction. We redesign intake and prioritization, so demand meets real capacity, and rebuild the path from request to release so planning, build, review, testing and deployment connect to each other instead of handing off through spreadsheets and status meetings.   Most of our build work sits upstream, where the cost of rework gets set: requirements generation, user story synthesis, design review, architecture validation. That means creator and reviewer agent patterns, tool-use orchestration, and multi-agent workflows on enterprise frameworks, with a working understanding of what each pattern costs in tokens and where it breaks. We treat the lifecycle as a system and a product itself - built to operate at a speed required for the AI era.   Then we make it stick . Engineering teams move to AI-augmented work and stay there, which is what separates a delivery organization where AI is how software gets made from the working pilot.  Practitioner depth in delivery operating models and AI, paired with Accenture's global platform and technology expertise , puts us in front of delivery organizations at a scale few firms reach.   You Are The AI SDLC Transformation Manager guides enterprise clients on redesigning software delivery to be AI-native.  You've spent real time in software delivery , not just studying it. You know what broken looks like at the process level, and you know what good looks like when AI is woven in deliberately rather than bolted on.   The Work This role applies flow metric analysis to surface waste in existing processes, designs future-state AI-native workflows, and builds agentic systems for upstream SDLC phases (define, design, requirements). The Advisor brings deep fluency in the evolving AI tooling landscape and translates what digitally native engineering organizations are doing into actionable roadmaps for enterprise clients.       Role-Specific Responsibilities:   Value stream mapping and flow metric analysis (cycle time, flow efficiency, WIP, throughput) to identify waste in existing SDLC practices   Design of AI-native to-be processes that restructure and accelerate how software is defined, designed, and governed   Change management for engineering teams transitioning to AI-augmented workflows   Strategy and d esign of agentic systems targeting upstream SDLC phases: requirements generation, user story synthesis, design review, and architecture validation   Know how and where to use creator and reviewer agent patterns an d  the token impact of doing so.   Model SDLC as a system, identifying feedback loops, bottlenecks, and second-order effects of process changes   Cross-functional perspective spanning product, engineering, QA, security, and platform   Ability to maintain fluency in the rapidly evolving AI tooling ecosystem (coding assistants, agentic platforms, evaluation frameworks, MCP/tool orchestration)   Ongoing research practice tracking capability shifts, new model releases, and emerging practitioner patterns   Expertise in what digitally native organizations are doing today and ability to translate those benchmarks into actionable roadmaps for enterprise clients   Ability to influence clients to change and modernize processes that limit the positive impact of agentic ways of working and overall SDLC   Travel may be required for this role.  The amount of travel will