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About This Role
Security Consulting Manager \| Senior Level \| Full time
Job No. R00341230 \| Multiple Locations
We Are
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.
We are Secure, Responsible AI \& Data Protection professionals who enable trust, resilience, and compliance across the AI lifecycle and data estate; including securing AI systems and agents against emerging threats like prompt injection, model manipulation, and unauthorized agent actions, applying AI to strengthen cyber defense through faster detection, triage, and response, and protecting sensitive data through classification, data loss prevention, and governed access, so that enterprises can adopt AI and agentic capabilities confidently while safeguarding data and meeting their regulatory obligations.
We are currently looking for professionals for our Secure, Responsible AI \& Data Protection practice with extensive experience in the following:
You Are
Managers are the hands\-on delivery engine of the Secure AI practice. They lead day\-to\-day execution of client workstreams with a high degree of independence, produce the technical work product that defines Accenture's quality, and develop consultants into capable practitioners. Each Manager hire will be expected to operate across multiple Secure AI disciplines, with a focus on Security for AI and AI for Security, while operating from a shared technical foundation. Managers are expected to be deeply productive with agentic coding tools, build and run security tooling, facilitate client workshops, manage small teams, and contribute to practice\-building assets including playbooks, reusable tools, and training materials.
The Work (Role Responsibility):
- Cybersecurity Secure AI solutions that transform clients’ Enterprise AI adoption and secure their AI\-enabled operations
- Design, set\-up, and test prototype and production secure AI deployment solutions and ensure that all the pieces work together seamlessly
- Work with the project team, team leaders, project delivery leads, and client stakeholders to create stand\-out AI Security and Governance offerings powered by LLM platforms, frontier grade models, agentic frameworks, and AI/MCP gateways (Anthropic Claude, OpenAI, Azure OpenAI as representative examples
- Develop strong relationships with clients and gain the trust of key advisors
- Make the business case for the most effective and appropriate secure AI solution recommended to the client
- Pitch in on Accenture sales efforts when needed
- Continue to learn and develop your technical foundational AI, AI security, emerging technology and business expertise
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 5 years of total experience in Cybersecurity discipline with depth in at least one area: AppSec, SecOps / detection engineering, IAM, cloud security, offensive security / penetration testing, GRC, or responsible AI
- Minimum 1 year of experience Hands\-on proficiency with agentic coding tools: Claude Code, Cursor, GitHub Copilot, or Codex — active daily use required
- Minimum 1 year of experience with AI architecture fundamentals: LLMs, agentic systems, A2A, RAG, MLOps pipelines, MCP, AI Gateway, model serving
- Minimum 1 year of experience in AI\-specific security: OWASP LLM Top 10, prompt injection, adversarial inputs, AI supply chain risks
- Minimum 1 year ofexperience in threat modeling for AI systems (STRIDE, PASTA, or equivalent) applied to LLM deployments and agentic pipelines
- Cloud platform security fundamentals certification (AWS, Azure, or GCP)
- 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
- Technical delivery leadership: producing and reviewing security assessments, architecture designs, tooling, and written findings
- Build strong client relationships and partner with organizations to reimagine their business through innovation
- Collaborate across diverse communities—including domain experts, engineers, and designers
- Clear communication of technical findings to director\-level client stakeholders
- Structured problem decomposition and methodical troubleshooting
- Mentoring and developing consultant\-level practitioners through delivery
- Proven success in contributing to a team\-oriented environment
- Proven ability to work creatively and analytically in a problem\-solving environment
- Desire to work in an environment fostering teaming
- Excellent leadership, communication (written and oral), and interpersonal skills
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 08/21/2026\.
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
Atlanta, GA
Albany, NY
Arlington, VA
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
Irving, 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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Candidates who are currently employed by a client of Accenture or an affiliated Accenture business may not be eligible for consideration.
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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 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 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 $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 ($190K) sits 13% below the category median. Disclosed range: $87K to $293K.
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.
Logic, Inc. AI Hiring
Logic, Inc. has 17 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, AI Architect. Positions span Seattle, WA, US, New York, NY, US, Columbus, OH, US. Compensation range: $205K - $387K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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