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About This Role
We Are:
A cybersecurity consulting practice focused on helping organizations build real resilience—before, during, and after an incident. We design and run high\-impact client engagements, and we build the data\-driven tools that make those engagements better. Our team doesn’t just deliver advice; we create the structured frameworks, interactive assets, and analytical products that clients take home and actually use. We’re growing a capability at the intersection of cybersecurity expertise and applied data and development skills—and we want people who can do both.
You Are:
A data\-minded cybersecurity consultant who builds things. You're as comfortable in front of a client room as you are in a Python notebook or low\-code development environment. You think in structures—how do you capture the right inputs, surface the right insights, and produce outputs that drive decisions? You have a strong foundation in cyber readiness and recovery concepts, and you use that knowledge to design tools that make engagements sharper and scalable. You're familiar with Azure, comfortable using AI as a development accelerator, and you care deeply about the quality and usability of what you produce. You pick up new domains, tools, and environments quickly—and you bring that same curiosity to every engagement.
You bring executive presence to client interactions, can translate complex technical concepts for any audience, and stay composed and focused when the path forward isn't fully defined.
The Work:
- Design and build proprietary tools, templates, and data products that support cyber readiness and recovery engagements—turning repeatable consulting work into structured, reusable assets
- Translate engagement workflows and client outcomes into functional prototypes using low\-code platforms, AI\-assisted development, and data visualization tools (Power BI, Azure Data services, Python, or similar)
- Own the full arc of a client engagement: from scoping and facilitation through synthesis, analysis, and delivery of polished, actionable outputs
- Work with data throughout—structuring inputs, analyzing results, and producing frameworks and reports that are clear, defensible, and decision\-ready
- Collaborate with senior practitioners to identify where new tooling can improve consistency, speed, or quality across the practice’s service offerings
- Govern the tools you build: documentation, version control, onboarding guides, and iteration based on real\-world use
- Build trusted client relationships; serve as a credible, articulate voice on cyber resilience strategy and the practical value of data\-driven approaches
- Contribute to practice development—proposals, capability decks, and internal knowledge\-sharing
- Travel to client sites as needed (estimated 25–50%); primarily Monday–Thursday cadence
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 experience spanning cybersecurity consulting and data\-oriented development or analysis
- Minimum of 4 years of client\-facing or strong consulting experience: you can lead a room, facilitate a difficult conversation, and present complex findings in plain language to diverse audiences, including executives
- Minimum of 4 years of demonstrated ability to build something—a tool, dashboard, structured template, or automated workflow—that others actually use in professional practice
- Minimum of 4 years of experience producing structured deliverables with analytical substance—not just slide decks, but frameworks, models, or data products that reflect real depth
- Minimum of 2 years of hands\-on experience in Microsoft Azure environments; familiarity with Azure data and analytics services is a strong plus
- Minimum of 4 years of demonstrated experience in least one data or development layer: Python, Power BI, Power Apps/Automate, SQL, or a comparable stack
- Minimum of 1 year using AI coding assistants and generative tools as part of your everyday development workflow—not just occasionally, but fluently
- Bachelor's degree in Cybersecurity, Information Systems, Data Science, Computer Science, or equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience)
Bonus Points If:
- You’ve built tools or assets that were adopted and scaled across a team or practice, not just used once
- You have experience designing structured decision\-support frameworks or analytical products for security or risk use cases
- You hold a relevant certification: CISSP, CISM, SC\-200, AZ\-500, PL\-300, or equivalent
- You have experience with AI governance, responsible use frameworks, or tool validation in a security context
- You’ve contributed to a CoE, internal capability build, or practice development initiative
- You have experience with KQL, Sentinel Workbooks, or similar query/visualization layers in Azure
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 07/30/2026\.
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 $70,350 to $205,800
Cleveland $59,100 to $164,600
Colorado $63,800 to $177,800
District of Columbia $68,000 to $189,300
Illinois $59,100 to $177,800
Maine $54,400 to $151,400
Maryland $63,800 to $177,800
Massachusetts $63,800 to $189,300
Minnesota $63,800 to $177,800
New York $66,300 to $205,800
New Jersey $68,000 to $205,800
Virginia $59,100 to $189,300
Washington $80,200 to $189,300
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 $59K-$205K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($132K) sits 28% below the category median. Disclosed range: $59K to $205K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Accenture AI Hiring
Accenture has 7 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Morristown, NJ, US, New York, NY, US. Compensation range: $205K - $338K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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