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About This Role
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.
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 is woven in deliberately 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 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)
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
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
Requesting an Accommodation
Accenture is committed to providing equal employment opportunities for persons with disabilities or religious observances, including reasonable accommodation when needed. If you are hired by Accenture and require accommodation to perform the essential functions of your role, you will be asked to participate in our reasonable accommodation process. Accommodations made to facilitate the recruiting process are not a guarantee of future or continued accommodations once hired.
If you would like to be considered for employment opportunities with Accenture and have accommodation needs such as for a disability or religious observance, please call us toll free at 1 (877) 889-9009 or send us an email or speak with your recruiter.
Equal Employment Opportunity Statement
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For details, view a copy of the Accenture Equal Opportunity Statement
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Other Employment Statements
Applicants for employment in the US must have work authorization that does not now or in the future require sponsorship of a visa for employment authorization in the United States.
Candidates who are currently employed by a client of Accenture or an affiliated Accenture business may not be eligible for consideration.
Job candidates will not be obligated to disclose sealed or expunged records of conviction or arrest as part of the hiring process. Further, at Accenture a criminal conviction history is not an absolute bar to employment.
The Company will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. Additionally, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the Company's legal duty to furnish information.
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Salary Context
This $87K-$266K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Accenture, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($176K) sits 17% below the category median. Disclosed range: $87K to $266K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Accenture AI Hiring
Accenture has 42 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Architect, Prompt Engineer. Positions span New York, NY, Mountain View, CA, US, Morristown, NJ, US. Compensation range: $218K - $434K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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