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About This Role
Posted Date 3/09/2026
Description
We Are
Accenture is recognized as a global leader in AI and cloud transformation, helping businesses across industries migrate, manage, and optimize their cloud environments. Through partnerships with leading cloud providers such as Nvidia, AWS, Microsoft Azure, and Google Cloud, Accenture offers end\-to\-end services that drive innovation and business agility.
The Cloud Advisory Practice focuses on helping organizations define, plan, and implement innovative AI and cloud strategies that drive business value. Leveraging deep expertise across cloud platforms and technologies, this practice works collaboratively with clients to design scalable, secure, and resilient cloud environments. The practice offers guidance in key areas such as agentic AI infrastructure \& hosting, modern cloud foundation, security and resiliency, full\-stack FinOps, and cloud\-native development approaches, ensuring that clients achieve agility, operational efficiency, and long\-term growth. By aligning AI and cloud initiatives with business goals, the practice helps organizations realize the full potential of cloud innovation while navigating industry\-specific challenges and regulations
You Are
A Cloud Architect interested in solving some of the hardest problems in enterprise AI transformation—designing multi\-agent systems that actually work, building composable architectures that blend AI and traditional distributed systems, and transforming established industries.
The Work
Most software developers, engineers, and architects get to build software systems. Very few get to transform how industries operate. We're combining AI technology, industry expertise, and entrepreneurial experience to fundamentally reimagine core business processes across financial services, healthcare, procurement, retail, and logistics. We partner with our clients to build better products and experiences at enterprise scale.
- We're doing this by leveraging agentic architectures, multi\-agent orchestration patterns, and composable AI systems alongside proven distributed patterns (Event Sourcing, Event\-Driven Architecture, Microservices, Domain\-Driven Design, CQRS) and technologies such as (Claude API, Neo4j, Qdrant, PostgreSQL, event streaming platforms, vector databases, cloud platforms) to build AI\-native solutions for the enterprise.
- Most of our work fits what we call AI Transformation Decoupling: we design and build state\-of\-the\-art agentic systems to wrap legacy cores, establish real\-time feedback loops, add new AI\-native functionality, and methodically transform existing systems into composable, event\-driven architectures that support human\-AI collaboration at scale.
- Before we change anything, we use AI agents to systematically understand what exists—mapping dependencies, analyzing git history, discovering hidden coupling, and identifying knowledge concentration—transforming months of manual analysis into days of comprehensive intelligence. This means transformations informed by reality, not assumptions, and changes that don't break production because we discovered the constraints first. You'll learn patterns proven in production, understand when each applies and why, and build systems that scale because they're architecturally sound—not just technically sophisticated.
- Our team is deeply hands\-on, highly technical, and prides itself on being battle\-hardened, lead\-from\-the\-front AI transformation thought leaders
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 3 years of hands\-on experience building interesting and innovative applications, with at least 1 year working with AI/LLM systems in production or production\-like contexts.
- Minimum of 3 years of experience explaining complex AI concepts to executive audiences and translating between technical capabilities and business value.
- Minimum of 2 year of experience designing and building software systems, including planning AI\-native architectures, infrastructure, and integration patterns.
- Minimum of 5 years of experience leading an agile team and managing the unique challenges of AI development (iteration on prompts, dealing with non\-determinism, managing costs).
- Minimum of 1 year of experience designing engineering systems and DevOps for AI workloads (model deployment, monitoring, version control for prompts).
- Minimum of 1 year of understanding of the economics of AI systems (token costs, latency tradeoffs, when to fine\-tune vs. prompt)
- 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
- Experience building with LLM APIs (Claude, GPT\-4, etc.) and understanding prompt engineering patterns
- Experience designing multi\-agent systems with distinct roles (planning, execution, evaluation, coordination)
- Experience designing and building MCP Server and Client including standard connection, tools and data exposure as well as specific tools
- Experience with agentic frameworks (LangChain, LlamaIndex, or custom orchestration patterns)
- Experience analyzing and transforming existing systems—understanding legacy architectures through systematic dependency analysis, git history mining, and architectural discovery before modification (brownfield work is most of enterprise AI)
- Hands\-on experience with vector databases and RAG architectures (Qdrant, Pinecone, ChromaDB, Weaviate)
- Understanding of graph databases and knowledge graphs (Neo4j, Neptune) for semantic relationships and ontology modeling
- Hands\-on experience with cloud platforms (AWS, Azure, or GCP)—specifically choosing and configuring components for AI\-native architectures
- Experience with event\-driven architectures and streaming technologies (Kafka, Kinesis, EventBridge, event streaming platforms) for real\-time AI feedback loops
- Experience with microservices architectures and composable system design
- Experience with containerization and orchestration (Docker, Kubernetes, ECS)
- Understanding of observability and monitoring for AI systems (LLM tracing, token usage, latency, cost tracking)
- Experience with production AI operations—LLMOps, prompt versioning, model lifecycle management, or managing AI systems at scale
- Experience with real\-time communication protocols (WebSockets, Server\-Sent Events, HTTP/2\) for human\-AI interaction patterns
- Experience with distributed transactional data stores and their consistency models
- Functional programming experience, particularly patterns relevant to AI systems (immutability, pure functions, composition)
- Understanding of information retrieval, semantic search, or embedding\-based similarity
- Prior experience in traditional ML/data science (helpful but not required—we're often doing something quite different)
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 07/31/2026\.
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
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
We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.
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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We work with one shared purpose: to deliver on the promise of technology and human ingenuity. Every day, more than 775,000 of us help our stakeholders continuously reinvent. Together, we drive positive change and deliver value to our clients, partners, shareholders, communities, and each other.
We believe that delivering value requires innovation, and innovation thrives in an inclusive and diverse environment. We actively foster a workplace free from bias, where everyone feels a sense of belonging and is respected and empowered to do their best work.
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Join Accenture to work at the heart of change. Visit us at www.accenture.com.
Salary94,400\.00 \- 266,300\.00 Annual
Type
Full\-time
Salary Context
This $87K-$266K range is below 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($176K) sits 19% below the category median. Disclosed range: $87K to $266K.
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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