AI SDLC Transformation Manager

Accenture•St. Louis, MO

4d•$80,400 - $266,300•Hybrid

About The Position

Accenture's Technology Strategy & Transformation Organization, specifically the Strategic Technology Effectiveness team, is rebuilding how large enterprises define, design, and govern software. They design and implement agentic software and processes to run it, fundamentally changing the operating model, ways of working, and tooling of engineering organizations. Their approach begins with evidence, using metrics like cycle time, flow efficiency, WIP, and throughput to identify bottlenecks. The solutions developed are AI-native by construction, redesigning intake and prioritization to align demand with capacity, and rebuilding the path from request to release for seamless planning, build, review, testing, and deployment. A significant focus is on upstream SDLC phases such as requirements generation, user story synthesis, design review, and architecture validation, employing creator and reviewer agent patterns, tool-use orchestration, and multi-agent workflows. The software lifecycle is treated as a system and a product, built for the speed of the AI era. The team ensures these AI-augmented work practices are adopted and sustained by engineering teams, differentiating them from pilot programs. This is achieved through deep expertise in delivery operating models and AI, combined with Accenture's global platform and technology capabilities.

Requirements

Minimum of 5 years working across the full SDLC, requirements through release, with demonstrated ability to map, measure, and redesign delivery processes.
Minimum of 3 years of hands-on experience designing or building multi-agent or LLM-integrated systems, including prompt engineering, tool-use orchestration, or agentic application development.
Minimum of 3 years in a consulting or advisory capacity working with engineering organizations, ideally at enterprise scale (5,000+ employees or $1B+ revenue).
Minimum of 3 years applying flow metrics (cycle time, WIP, throughput, flow efficiency) or value stream mapping as active diagnostic tools.
Minimum of 2 years working directly with AI/ML systems, LLMs, or agentic architectures in a professional context.
Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience).

Nice To Haves

Hands-on experience with 2+ leading AI coding assistants (GitHub Copilot, Cursor, Amazon Q Developer, Cody) in enterprise environments.
Experience designing lightweight LLM evals for AI-generated SDLC artifacts (requirements docs, user stories, design reviews).
Working knowledge of Model Context Protocol (MCP) and emerging tool orchestration standards.
Fluency in at least one major delivery methodology (Agile, SAFe, continuous delivery) with a clear point of view on where AI creates the most leverage.
Track record of presenting AI transformation strategies to VP- or C-level stakeholders and getting buy-in.

Responsibilities

Guides enterprise clients on redesigning software delivery to be AI-native.
Applies flow metric analysis to surface waste in existing processes.
Designs future-state AI-native workflows.
Builds agentic systems for upstream SDLC phases (define, design, requirements).
Translates what digitally native engineering organizations are doing into actionable roadmaps for enterprise clients.
Performs value stream mapping and flow metric analysis (cycle time, flow efficiency, WIP, throughput) to identify waste in existing SDLC practices.
Designs AI-native to-be processes that restructure and accelerate how software is defined, designed, and governed.
Manages change for engineering teams transitioning to AI-augmented workflows.
Develops strategy and designs agentic systems targeting upstream SDLC phases: requirements generation, user story synthesis, design review, and architecture validation.
Applies creator and reviewer agent patterns and understands their token impact.
Models SDLC as a system, identifying feedback loops, bottlenecks, and second-order effects of process changes.
Maintains a cross-functional perspective spanning product, engineering, QA, security, and platform.
Maintains fluency in the rapidly evolving AI tooling ecosystem (coding assistants, agentic platforms, evaluation frameworks, MCP/tool orchestration).
Conducts ongoing research tracking capability shifts, new model releases, and emerging practitioner patterns.
Leverages expertise in what digitally native organizations are doing today and translates those benchmarks into actionable roadmaps for enterprise clients.
Influences clients to change and modernize processes that limit the positive impact of agentic ways of working and overall SDLC.