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About This Role
Security Consulting Manager \| Senior Level \| Full time
Job No. R00335757 \| Multiple Locations
We Are
Accenture Security helps organizations prepare, protect, detect, respond, and recover along with all points of the security lifecycle. Cybersecurity challenges are different for every business in every industry. Leveraging our global resources and advanced technologies, we create integrated, turnkey solutions tailored to our client's needs across their entire value chain. Whether we’re defending against known cyberattacks, detecting and responding to the unknown, or running an entire security operations center, we will help companies build cyber resilience to grow with confidence. Our team of the security sector’s brightest people uses the coolest tech to out\-hack the hackers and help clients build resilience from within. We blend risk strategy, digital identity, cyber defense, application security, and managed service solutions to rethink the entire security lifecycle.
Do you have the deep functional and technical experience to help implement security solutions that align with our clients’ business objectives? Do you have the expertise to design and deliver solutions for establishing system user's credentials, and processes for applying those credentials to access enterprise systems and applications?
If so, read on and apply. Accenture’s more than 2,000 security professionals deliver holistic and proactive security solutions in 47 countries, and we’d love to discuss our open Identity and Access Management (I\&AM) Security role with you
You Are
A cyber security savant. You’ve got the skills and experience to keep data safe from black hat hackers or other threats, whether by coding a threat intrusion module or sharing your latest ideas with the team. Day\-to\-day, you’re all about solving security problems for clients and using your people skills to make sure everyone on your team is working well and happily.
You are passionate about security, love what you do and have a genuine desire to outsmart the bad guys. You have the experience to analyze a clients’ security posture, anticipate security requirements and help find right\-sized solutions based on industry leading practices. You have a proven track record working successfully in a fast\-paced, team\-oriented environment. You’re a creative, analytical problem solver with above average documentation skills who can speak to both technical and non\-technical audiences. You are eager to put your skills to use by helping us help our clients inject security at every level of their organization.
The Work
We are a global collective of innovators applying the New every day to improve the way the world works and lives. Help us show the world what’s possible as you partner with clients to unlock hidden value and deliver innovative solutions. Empowered with innovative tools, continuous learning, and a global community of diverse talent and perspectives, we drive success in a new business architecture that disrupts conventional practices. Our expertise spans 40\+ industries across 120\+ countries and impacts millions of lives every day. We turn ideas into reality. Specialist Forward Deployed Engineers focused on the emerging agentic AI identity stack, designing and implementing identity solutions for AI agents, MCP servers, and autonomous workflows. This role sits at the intersection of identity engineering and applied AI security.
Although no two days at Accenture are the same, your duties as Identity \& Access Management (I\&AM) Security Manager likely will include:
- Working directly with clients and Accenture teams leading multi\-disciplined global teams to design, implement and operate I\&AM solutions.
- Developing deep working relationships with Senior Executives and Senior Managers across the client account team and client.
- Understanding the business direction of companies and creating optimized I\&AM architectures to meet their business needs.
- Building knowledge capital through research and development and leveraging industry insights to deliver best of breed expertise to clients.
- Helping grow Accenture Security across North America through thought leadership and entrepreneurialism
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 of software engineering experience with recent hands\-on Python and async work
- Minimum of 3 years and working knowledge of OAuth 2\.0 and OIDC flows including DCR, PAR, and token exchange
- Minimum of 3 years of experience with at least one agent framework (Claude Agent SDK, LangGraph, AutoGen, CrewAI, Semantic Kernel)
- Minimum of 1 year of MCP server development or integration experience
- Minimum of 4 years of experience and deep understanding of Zero Trust architectures, Identity Governance and Administration (IGA), Secrets Management (CyberArk, HashiCorp), and policy\-based access control.
- Minimum of 5 years of Cloud platforms (AWS, GCP, Azure), containerization (Docker, Kubernetes), IaC (Terraform), and CI/CD pipelines experience
- Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience)
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 07/26/2026\.
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 $293,800
Cleveland $87,400 to $235,000
Colorado $94,400 to $253,800
District of Columbia $100,500 to $270,300
Illinois $87,400 to $253,800
Maine $80,400 to $216,200
Maryland $94,400 to $253,800
Massachusetts $94,400 to $270,300
Minnesota $94,400 to $253,800
New York $87,400 to $293,800
New Jersey $100,500 to $293,800
Virginia $87,400 to $270,300
Washington $100,500 to $270,300
Irving, TX
Albany, NY
Arlington, VA
Atlanta, GA
Austin, TX
Beaverton, OR
Bentonville, AR
Boston, MA
Carmel, IN
Charlotte, NC
Chicago, IL
Cincinnati, OH
Cleveland, OH
Columbus, OH
Culver City, CA
Denver, CO
Des Moines, IA
Detroit, MI
Hartford, CT
Houston, TX
Kirkland, WA
Miami, FL
Milwaukee, WI
Minneapolis, MN
Morristown, NJ
Mountain View, CA
Nashville, TN
New York City, NY
Oklahoma City, OK
Overland Park, KS
Philadelphia, PA
Pittsburgh, PA
Raleigh, NC
Redmond, WA
Sacramento, CA
San Diego, CA
San Francisco, CA
Scottsdale, AZ
Seattle, WA
St. Louis, MO
St. Petersburg, FL
Walnut Creek, CA
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Salary Context
This $87K-$293K range is above the median 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 Logic, Inc., 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. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $87K to $293K.
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.
Logic, Inc. AI Hiring
Logic, Inc. has 8 open AI roles right now. They're hiring across AI Safety, AI/ML Engineer, AI Software Engineer. Positions span New York, NY, US, Irving, TX, US, Arlington, VA, US. Compensation range: $168K - $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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