AI SDLC Transformation Manager

Accenture; Detroit (MI); On-site; USD 95,000 - 180,000; Full time

Benefits offered by this job

Medical benefits

Dental benefits

Vision benefits

Life insurance

401(k) plan

Bonus opportunities

Paid holidays

Paid time off

Job summary

Accenture seeks an AI SDLC Transformation Manager to guide enterprise clients in redesigning software delivery to be AI-native. You will surface waste via flow metrics, design future-state AI-native workflows, and build agentic systems for upstream SDLC phases, aligning with evolving AI tooling and enterprise roadmaps.

The role emphasizes hands-on experience across the full SDLC, multi-agent/LLM integration, and consulting at scale, with a compensation model reflecting national roles and travel

Qualifications

• 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 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 context
• Bachelor's degree or equivalent (minimum 12 years) work experience.

Responsibilities

• Value stream mapping and flow metric analysis 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.

Skills

Flow metrics

AI/LLM systems

Prompt engineering

Tool orchestration

Agentic architectures

Consulting experience

Education

Bachelor's degree

Job description

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 andprioritization,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 separatesadelivery organization where AI is how software gets madefrom theworking 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 iswovenindeliberately 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 vary from 0 to 100% depending on business need and client requirements.

Here’s WhatYou Need:

• Minimum of 5 yearsworking across the full SDLC , requirements through release , with demonstrated ability to map, measure, and redesign delivery processes

• Minimum of 3 yearsof 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 yearsin a consulting or advisory capacity working with engineering organizations, ideally at enterprise scale (5,000+ employees or $1B+ revenue)

• Minimum of 3 yearsapplying 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 yearsworking 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)

Bonus points if you have

• 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

Compensation at Accenture varies depending on a wide array of factors, which may include but are not limited to the specific office location, role, skill set, and level of experience. As required by local law, Accenture provides a reasonable range of compensation for roles that may be hired as set forth below.We anticipate this job posting will be posted until 11/30/2026.

Accenture offers a market competitive suite of benefits including medical, dental, vision, life, and long-term disability coverage, a 401(k) plan, bonus opportunities, paid holidays, and paid time off. See more information on our benefits here:

U.S. Employee Benefits | Accenture (https://www.accenture.com/us-en/careers/local/benefits)

Role Location Annual Salary Range

California $94,400 to $266,300

Cleveland $87,400 to $213,000

Colorado $94,400 to $230,000

Columbus $87,400 to $213,000

Connecticut $94,400 to $230,000

District of Columbia $100,500 to $245,000

Illinois $87,400 to $230,000

Maine $80,400 to $196,000

Maryland $94,400 to $230,000

Massachusetts $94,400 to $245,000

Minnesota $94,400 to $230,000

New York $87,400 to $266,300

New Jersey $100,500 to $266,300

Virginia $87,400 to $245,000

Washington $100,500 to $245,000

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