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About This Role
Client Account Management Manager \| Senior Level \| Full time
Job No. R00322773 \| Multiple Locations
We are:
Accenture Song accelerates growth and value for our clients through sustained customer relevance. Our capabilities span ideation to execution: growth, product and experience design; technology and experience platforms; creative, media and marketing strategy; and campaign, content and channel orchestration. With strong client relationships and deep industry expertise, we help our clients operate at the speed of life through the unlimited potential of imagination, technology and intelligence. Visit us at: www.accenture.com/song
The Content Practice, inside the Marketing pillar of Song, is where we partner with clients to help them capture, create, produce, and localize content as efficiently and effectively as possible. We partner with clients across industries, and we are able to bring capabilities that span consulting and transformations services (op model and technology road mapping and stand up), to film/video/photo capture/post production, and scaled globally delivered, tech powered, managed services around content capture, creation and production.
About the Role
- We are seeking a dynamic and forward‑thinking Manager, Generative AI \& Content Transformation to join our Content workstream. This leader will play a key role in shaping how our clients leverage AI to modernize content operations, improve content workflows, and adopt next‑generation marketing and content technologies.
- The ideal candidate brings a blend of Generative AI fluency, Consulting experience, Client Delivery expertise, and a strong understanding of Content Operations at scale.
- This is not an AI engineering role; rather, it's a role for someone who can translate AI capabilities into business‑ready solutions, guide teams, and confidently engage with clients.
- As a manager, you will be a critical strategic advisor that is responsible for leading parts of the development of \- and playing a hands on role in delivering \- highly strategic, innovative and tailored client solutions
Key Responsibilities
Content Automation and GenAI – Consulting and Delivery
- Have a conceptual understanding of GenAI tools and automation within the content ecosystem of technologies
- Evaluate and differentiate between major platforms and tools (e.g., OpenAI, Google Gemini, M365 Copilot, Writer, FireFly, ChatGPT, Adobe Workfront, AEM, etc).
- Quickly research and assess emerging AI tools and propose the right solution based on client needs, use cases, data considerations, and constraints.
- Translate AI and GenAI concepts into clear, client‑friendly language; educate stakeholders on what’s possible, what’s practical, and what’s required.
- Lead and support delivery of GenAI proof of concepts (POCs), prototypes, and pilot programs.
- Stay ahead of fast‑moving trends in AI, content technologies, and next‑gen marketing, ensuring the team and clients benefit from emerging innovations.
Content Transformation
- Understand and assess client content ecosystems, including content creation, workflows, governance, CMS/DAM systems, metadata, content taxonomies, and content operations at scale.
- Partner with content strategy, technology, and operations teams to reimagine content processes using automation, AI, and modern content tooling.
- Evaluate content technology applications and help clients build modernization roadmaps in partnership with various Accenture capability groups.
- Support design of scalable content workflows and operating models enabled by major pla AI.
Delivery \& Team Leadership
- Lead cross\-functional teams across strategy, design, content, and scaled delivery operations to deliver high‑quality client work.
- Manage and mentor junior team members; foster a culture of curiosity, experimentation, and learning.
- Conceptually understand modern product development methodologies (Agile, rapid prototyping, iterative delivery) to structure work and drive outcomes.
Client Engagement \& Business Development
- Act as a trusted advisor to clients, confidently communicating GenAI concepts, opportunities, risks, and solution options.
- Support business development efforts including RFP responses, proposal creation, solutioning, and case studies.
- Identify new opportunities to apply GenAI and content transformation solutions within existing and new accounts.
- Contribute to thought leadership, client workshops, and strategic planning discussions.
Basic Qualifications:
- 7\+ years of client‑facing consulting or digital transformation experience. Experience must include:
+ Hands‑on experience researching, evaluating, and applying content automation or AI tools in real\-world business contexts.
+ Experience delivering GenAI prototypes or POCs, even if not as a technical builder.
+ Strong understanding of AI and GenAI fundamentals, including LLM concepts, prompt engineering basics, model capabilities/limitations, safety, and responsible AI considerations.
+ Familiarity with content transformation, content creation workflows, content supply chains, and/or content operations.
+ Strong communication skills with the ability to translate technical concepts for non‑technical audiences.
+ Natural curiosity, continuous learner mindset, and excitement about emerging technologies and next‑generation content/marketing practices.
+ Experience managing teams and/or leading delivery workstreams across many capabilities and geographies
Preferred (Nice to Have)
- Experience with enterprise content systems (CMS, DAM, PIM, content automation platforms).
- Exposure to content metadata design, content modeling, or structured content.
- Excellent oral and written communicator, with great interpersonal skills; able to forge trusting relationships within a diverse team
What Success Looks Like
- You can confidently discuss GenAI solutions with clients — not as a deep engineer, but as a solution‑oriented expert.
- You know how to evaluate emerging AI tools and recommend the right one for a specific business context.
- You bring structure, clarity, and strategic thinking to ambiguous client problems.
- You build strong relationships across teams and help scale content transformation solutions.
- You can lead client delivery work
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 05/15/2026\. 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 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 Washington $100,500 to $245,000
New York City, NY
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
Herndon, VA
Houston, TX
Irvine, CA
Irving, TX
Kirkland, WA
Miami, FL
Milwaukee, WI
Minneapolis, MN
Morristown, NJ
Mountain View, CA
Nashville, TN
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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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.
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Salary Context
This $87K-$266K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($176K) sits 6% above the category median. Disclosed range: $87K to $266K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Logic, Inc. AI Hiring
Logic, Inc. has 47 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Consultant. Positions span New York, NY, US, Atlanta, GA, US, Chicago, IL, US. Compensation range: $93K - $434K.
Location Context
AI roles in New York pay a median of $200,000 across 1,670 tracked positions. That's 9% above 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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