Accenture
San Francisco, CA
+ 42 other locations
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.
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.
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.
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
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 What You Need:
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)
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
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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