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About This Role
Posted Date 4/10/2026
Description
We Are:
We are the Accenture Microsoft Business Group (AMBG)—a unified team that brings together Accenture, Avanade, and Microsoft to help organizations transform with the full power of the Microsoft ecosystem.
We combine deep industry expertise, modern cloud and data capabilities, and next\-generation AI—including Microsoft Copilot, Azure OpenAI, Copilot Studio, Microsoft Foundry, Fabric, and AMBG’s own accelerators—to help clients modernize, innovate, and achieve measurable business outcomes.
From cloud modernization to AI\-enabled business reinvention, security, analytics, and industry\-specific solutions, we partner closely with clients to design, build, and scale transformative Microsoft\-powered solutions that create lasting impact across their enterprise.
You Are:
A commercially\-driven practitioner whose technical and domain depth makes you dangerous in a sales pursuit and in client delivery. You understand how agentic AI applies to process reinvention, governance, Centers of Excellence, operating model design, and the full agentic lifecycle well enough to shape a solution, challenge an architect, and give a CIO a reason to sign. You originate deals, guide delivery from a subject matter position, and bring the Microsoft AI breadth that makes the client conversation credible. The work you do before the SOW is signed is as valuable as the work you do in delivery.
The Work
As a Microsoft\-specialized Agentic AI Senior Manager in AMBG, your role will pivot on multiple axes; lead origination and pre\-sales solutioning for high\-impact agentic AI opportunities, bringing the technical and domain breadth to shape deals that others cannot, and across active engagements, you will operate as a Subject Matter Advisor: guiding solution direction, shaping governance and operating model design, and providing the senior technical and thought leadership that keeps programs on the right track. You carry both accountabilities simultaneously, and your delivery excellence is the credibility that opens the next pursuit.
Key Responsibilities
Subject Matter Advisory on Delivery
- Serve as Subject Matter Advisor across one or more active client engagements simultaneously: provide senior guidance on solution direction, architecture decisions, and technical risk to overall client programs.
- Guide delivery teams on AI governance design, agentic life\-cycle management, operating models, COE structure, stage\-gate policy, and responsible AI posture; bring the domain knowledge that delivery teams need to make the right calls without escalating every decision.
- Advise on Microsoft platform application across the agentic lifecycle: inform build approach selection (Copilot Studio vs. Azure AI Foundry pro\-code), orchestration patterns, RAG design, evaluation frameworks, and AIOps.
- Flag and guide resolution of security, compliance, and responsible AI risks before they become delivery problems; bring the enterprise deployment perspective that junior delivery team members are unlikely to carry.
- Contribute to phased roadmaps (POV to pilot to scaled rollout) and value tracking frameworks; ensure delivery milestones connect to the business outcomes committed in the SOW.
- Develop cross\-functional team capability in agentic AI delivery patterns; build reusable accelerators and repeatable approaches that reduce the need for SMA involvement over time.
Deal Shaping \& Pre\-Sales Solutioning
- Play a leadership role on agentic AI opportunities from origination to signed SOW: lead discovery, shape the value hypothesis, define the solution blueprint, and align commercial constructs to client outcomes; partner with AMBG sellers and Microsoft field teams throughout.
- Apply deep knowledge of agentic AI across process reinvention, governance frameworks, Centers of Excellence, operating model design, and the full agentic lifecycle to guide how Microsoft capabilities (365 Copilot, Copilot Studio, Azure AI Foundry, Azure OpenAI Service, Azure AI Search, Azure AI Agent Service, Azure ML, Microsoft Fabric) are positioned and scoped in a pursuit.
- Build defensible proposals and SOWs; define solution scope, risk and RAI posture, staffing model, and delivery approach at a level of depth that gives clients and internal deal teams confidence in what is being committed.
C\-level Storytelling \& Executive Advisory
- Craft board\-level narratives that connect industry problems to agentic AI outcomes; use data\-backed storytelling to secure executive sponsorship and funding.
- Facilitate executive workshops to shape transformation agendas, operating models, value cases, and governance from Copilots to fully autonomous agents.
Ecosystem Orchestration
- Operate as one with Accenture, Avanade, and Microsoft: co\-innovating, co\-selling, and co\-delivering; align to Microsoft programs and co\-funding where appropriate.
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 10 years in consulting or technology, including 3\+ years as a senior technical practitioner on cloud, data, or AI programs, ideally on Microsoft.
- Minimum of 7 years of experience crafting solution options, estimates, delivery models, risk mitigations; ability to collaborate with deal teams on pricing, terms and conditions, and governance.
- Minimum of 3 years of hands\-on architecture or supervision experience with Microsoft Foundry, Azure OpenAI Service, Azure AI Search, Azure AI Agent Service, Azure ML, Prompt Flow/evaluations, and Microsoft Fabric; fluency in when to use low\-code vs. pro\-code approaches.
- Minimum of 7 years of experience leading multi\-disciplinary teams across strategy, industry, data/AI engineering, and change management; strong coaching and lead\-from\-the\-front ethos.
- Bachelor’s degree or equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience.)
Professional Skills Requirement
- Practical knowledge of Responsible AI, identity, security, data governance, and deployment guardrails for enterprise AI.
- Strong C\-level communication and consulting skills; ability to engage business and technology stakeholders.
- Ability to lead large, multidisciplinary technical teams.
- Structured, value\-driven, problem\-solving mindset.
Bonus Points If:
- You’ve led end\-to\-end agentic AI business process transformations.
- You’ve shipped Copilot Studio solutions and Azure AI Foundry agents in regulated industries (FS, H\&PS), including content safety, evaluations, and monitoring.
- You bring industry POVs (e.g., Fabric\-enabled data modernization to agentic workflows to function transformation) and can connect them to measurable business KPIs.
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 08/14/2026\.
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 $132,500 to $302,400
Cleveland $122,700 to $241,900
Colorado $132,500 to $261,300
District of Columbia $141,100 to $278,200
Illinois $122,700 to $261,300
Maine $112,900 to $222,500
Maryland $132,500 to $261,300
Massachusetts $132,500 to $278,200
Minnesota $132,500 to $261,300
New York $122,700 to $302,400
New Jersey $141,100 to $302,400
Virginia $122,700 to $278,200
Washington $141,100 to $278,200
\#LI\-NA\-FY25
\#LI\-NA
\#LI\-MP
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Equal Employment Opportunity Statement
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Salary132,500\.00 \- 302,400\.00 Annual
Type
Full\-time
Salary Context
This $122K-$302K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Information Technology Senior Management Forum, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. Disclosed range: $122K to $302K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Information Technology Senior Management Forum AI Hiring
Information Technology Senior Management Forum has 44 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Architect, AI Safety. Positions span McLean, VA, US, San Jose, CA, US, New York, NY, US. Compensation range: $126K - $392K.
Location Context
AI roles in Chicago pay a median of $205,100 across 97 tracked positions. That's 6% below the national 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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