AI-Driven SDLC Transformation Leader

Accenture

Carmel (IN)

On-site

USD 120,000 - 230,000

Full time

14 days+

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Benefits offered by this job

Medical, dental, vision

401(k) plan

Paid holidays & time off

Job summary

Accenture is seeking an AI SDLC Transformation Manager to guide enterprise clients in redesigning software delivery to be AI-native. You will apply flow metrics to surface waste, design future-state AI-native workflows, and build agentic systems across upstream SDLC phases.

You will bring 5+ years in software delivery, 3+ years in multi-agent/LLM architectures, and 3+ years in consulting. Travel up to 100% may be required, with a strong emphasis on strategy, tool orchestration, and client

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 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) 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, minimum 6 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 reviews, 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 and bottlenecks.
Cross-functional perspective spanning product, engineering, QA, security, and platform.
Fluent with 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.
Ability to translate benchmarks into actionable roadmaps for enterprise clients.
Influence clients to modernize processes to maximize agentic SDLC impact.