AI SDLC Transformation Manager

Accenture•Boston, MA

1d•Hybrid

About The Position

The AI SDLC Transformation Manager guides enterprise clients on redesigning software delivery to be AI-native. 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.

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, not just frameworks you've presented on
Minimum of 2 years working directly with AI/ML systems, LLMs, or agentic architectures in a professional, not just experimental, 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

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 design 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 and 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