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About This Role
Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.
Location
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This is a remote role based in the US.
About The Job You're Considering
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The Enterprise AI Strategy \& Projects Practice Lead is accountable for the vision, growth, governance, operating model, portfolio, and business outcomes of the Enterprise AI Strategy \& Projects practice. This role serves as the senior leader responsible for turning AI opportunity into a scalable enterprise capability by aligning strategy, governance, delivery, engineering, adoption, financial accountability, and organizational transformation.
The Practice Lead owns the practice operating model and orchestrates a multidisciplinary team of AI strategy leaders, solution triage leaders, governance specialists, delivery leaders, fluency and adoption specialists, forward deployed engineers, DevOps leaders, GTM leaders, and supporting SMEs. The role ensures the practice operates as an integrated system rather than a collection of independent capabilities.
The Practice Lead is responsible for identifying market opportunities, shaping service offerings, building reusable intellectual property, driving client outcomes, growing delivery capability, developing talent, creating strategic partnerships, overseeing portfolio execution, and establishing Enterprise AI Strategy \& Projects as a recognized leader in enterprise AI transformation.
Your Role
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Practice Strategy \& Leadership
- Define and maintain the vision, mission, operating model, and strategic direction for the Enterprise AI Strategy \& Projects practice.
- Establish practice priorities that align market demand, client outcomes, business strategy, and emerging AI opportunities.
- Create a multi\-year roadmap for practice growth, capability development, service expansion, and market differentiation.
- Evaluate emerging AI trends, technologies, operating models, and industry shifts to determine strategic relevance.
- Serve as the senior accountable leader for practice performance, growth, execution quality, and value realization.
Practice Portfolio Management
- Maintain executive visibility across the AI opportunity and delivery portfolio.
- Balance investments across strategy, innovation, capability building, client delivery, workforce transformation, and operational improvement.
- Establish portfolio governance, prioritization models, escalation paths, investment criteria, and decision frameworks.
- Review and approve strategic initiatives entering practice execution.
- Ensure portfolio resources are aligned to the highest\-value opportunities and business priorities.
AI Strategy \& Advisory Oversight
- Provide executive oversight for enterprise AI strategy engagements, transformation programs, and innovation initiatives.
- Guide the development of AI roadmaps, business cases, operating models, investment strategies, and transformation plans.
- Ensure client AI strategies align with business goals, governance requirements, delivery realities, and value objectives.
- Advise senior executives on AI adoption, organizational readiness, portfolio priorities, and transformation approaches.
- Serve as an escalation point for strategic decisions requiring executive guidance.
Practice Operating Model \& Capability Development
- Design and continuously improve the operating model for Enterprise AI Strategy \& Projects.
- Define how strategy, triage, governance, delivery, fluency, engineering, DevOps, GTM, and FinOps capabilities work together.
- Build repeatable methods, frameworks, accelerators, templates, and delivery assets.
- Establish capability maturity objectives and improvement plans for the practice.
- Drive operational excellence through standardization, reuse, automation, and continuous improvement.
Leadership of Practice Functions
- Set priorities and accountability models across Enterprise AI Strategy, AI Security \& Governance, AI FinOps, Enterprise AI Delivery, AI Solution Triage, AI Fluency, Forward Deployed Engineering, Agentic DevOps, and AI GTM \& OCM.
- Ensure practice functions operate through coordinated handoffs, shared standards, and unified portfolio visibility.
- Resolve competing priorities across strategy, delivery, governance, engineering, adoption, and GTM workstreams.
- Coordinate investment decisions that improve capability, repeatability, delivery quality, and practice growth.
- Drive integrated execution across all workstreams so client outcomes are delivered through one cohesive practice model.
Responsibilities
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Revenue Growth \& Business Development
- Own practice growth objectives and market expansion strategies.
- Develop and mature service offerings, advisory capabilities, implementation services, and managed outcomes.
- Support executive sales activities, strategic pursuits, account planning, and opportunity qualification.
- Participate in executive client workshops, advisory sessions, and transformation discussions.
- Identify strategic partnerships that expand the practice’s reach and capability.
Client Executive Engagement
- Serve as a trusted advisor to executive stakeholders and senior client leadership.
- Help clients understand how AI affects operating models, workforce strategy, products, services, delivery models, and business performance.
- Lead high\-impact executive conversations concerning AI transformation and value realization.
- Facilitate resolution of strategic issues affecting enterprise AI programs.
- Build long\-term executive relationships that increase trust and market credibility.
Talent \& Workforce Leadership
- Build and develop a high\-performing team of AI leaders and specialists.
- Define role structures, career paths, capability models, and succession planning strategies.
- Recruit, mentor, and retain top AI talent.
- Establish a culture of innovation, accountability, collaboration, learning, and client impact.
- Promote knowledge sharing, capability transfer, and leadership development across the practice.
Governance, Risk \& Quality Oversight
- Ensure enterprise AI initiatives operate within appropriate governance, security, compliance, financial, and operational controls.
- Establish quality standards for strategy, advisory, engineering, delivery, and adoption engagements.
- Review major risks, delivery concerns, escalations, and portfolio challenges.
- Maintain executive visibility into practice health and operational performance.
- Create balance between innovation speed and enterprise control requirements.
Market Positioning \& Thought Leadership
- Establish the practice as a recognized leader in enterprise AI transformation.
- Represent the practice in executive forums, industry events, client briefings, and strategic discussions.
- Publish and promote reusable thought leadership, methodologies, frameworks, and AI transformation approaches.
- Identify emerging market demand and develop new offerings accordingly.
- Collaborate with GTM leaders to strengthen internal and external market visibility.
Your Skills And Experience
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Required Qualifications
- 15\+ years of experience in technology leadership, consulting, digital transformation, enterprise strategy, business leadership, AI transformation, or related executive roles.
- Proven experience building and leading multidisciplinary practices, business units, consulting organizations, or transformation functions.
- Experience overseeing large portfolios of strategic initiatives and technology programs.
- Strong understanding of enterprise AI, generative AI, automation, cloud platforms, governance, operating models, and organizational transformation.
- Experience engaging executive stakeholders and leading complex business transformation efforts.
- Strong capability in business development, practice management, financial planning, and organizational growth.
- Demonstrated leadership of cross\-functional teams across strategy, delivery, technology, and business domains.
Preferred Qualifications
- Experience with Microsoft Copilot, Azure OpenAI, OpenAI Enterprise, Anthropic Claude, Google Gemini, AWS Bedrock, or comparable enterprise AI ecosystems.
- Experience leading AI Centers of Excellence, transformation offices, innovation organizations, consulting practices, or technology portfolios.
- Familiarity with AI governance, Responsible AI, FinOps, cloud economics, platform strategy, and enterprise architecture.
- Experience developing market\-facing service offerings and transformation methodologies.
- Experience in consulting, professional services, managed services, enterprise technology organizations, or large\-scale business transformation environments.
- Relevant certifications in strategy, leadership, cloud, AI, enterprise architecture, transformation, finance, or organizational change.
Knowledge, Skills \& Abilities
- Strong executive leadership and organizational management capability.
- Ability to align strategy, governance, engineering, delivery, adoption, and operations into a cohesive transformation function.
- Deep understanding of AI business value, enterprise transformation, and operating model change.
- Strong portfolio management and prioritization skills.
- Ability to lead senior executives through complex decisions and organizational change.
- Strong business development and market\-growth capability.
- Strong communication, storytelling, facilitation, and executive\-influence skills.
- Ability to build sustainable, scalable, and repeatable enterprise capabilities.
Leadership Expectations
- Operate as the accountable executive for the Enterprise AI Strategy \& Projects practice.
- Build a high\-performing leadership team capable of scaling enterprise AI transformation.
- Drive alignment across all practice functions and stakeholders.
- Establish a culture of innovation balanced with governance, accountability, and measurable outcomes.
- Develop future practice leaders and succession pipelines.
- Create strategic differentiation in the market through capability, delivery excellence, and thought leadership.
- Ensure the practice consistently delivers measurable client and business impact.
The base compensation range for this role in the posted location is: $94,248 \- $191,950\.
Capgemini provides compensation range information in accordance with applicable national, state, provincial, and local pay transparency laws. The base compensation range listed for this position reflects the minimum and maximum target compensation Capgemini, in good faith, believes it may pay for the role at the time of this posting. This range may be subject to change as permitted by law.
The actual compensation offered to any candidate may fall outside of the posted range and will be determined based on multiple factors legally permitted in the applicable jurisdiction.
These may include, but are not limited to: Geographic location, Education and qualifications, Certifications and licenses, Relevant experience and skills, Seniority and performance, Market and business consideration, Internal pay equity.
It is not typical for candidates to be hired at or near the top of the posted compensation range.
In addition to base salary, this role may be eligible for additional compensation such as variable incentives, bonuses, or commissions, depending on the position and applicable laws.
Capgemini offers a comprehensive, non\-negotiable benefits package to all regular, full\-time employees. In the U.S. and Canada, available benefits are determined by local policy and eligibility and may include:
- Paid time off based on employee grade (A\-F), defined by policy: Vacation: 12\-25 days, depending on grade, Company paid holidays, Personal Days, Sick Leave
- Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)
- Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
- Life and disability insurance
- Employee assistance programs
- Other benefits as provided by local policy and eligibility
Important Notice: Compensation (including bonuses, commissions, or other forms of incentive pay) is not considered earned, vested, or payable until it becomes due under the terms of applicable plans or agreements and is subject to Capgemini’s discretion, consistent with applicable laws. The Company reserves the right to amend or withdraw compensation programs at any time, within the limits of applicable legislation.
Disclaimers
Capgemini is an Equal Opportunity Employer encouraging inclusion in the workplace. Capgemini also participates in the Partnership Accreditation in Indigenous Relations (PAIR) program which supports meaningful engagement with Indigenous communities across Canada by promoting fairness, accessibility, inclusion and respect. We value the rich cultural heritage and contributions of Indigenous Peoples and actively work to create a welcoming and respectful environment. All qualified applicants will receive consideration for employment without regard to race, national origin, gender identity/expression, age, religion, disability, sexual orientation, genetics, veteran status, marital status or any other characteristic protected by law.
This is a general description of the Duties, Responsibilities and Qualifications required for this position. Physical, mental, sensory or environmental demands may be referenced in an attempt to communicate the manner in which this position traditionally is performed. Whenever necessary to provide individuals with disabilities an equal employment opportunity, Capgemini will consider reasonable accommodations that might involve varying job requirements and/or changing the way this job is performed, provided that such accommodation does not pose an undue hardship. Capgemini is committed to providing reasonable accommodation during our recruitment process. If you need assistance or accommodation, please reach out to your recruiting contact.
Please be aware that Capgemini may capture your image (video or screenshot) during the interview process and that image may be used for verification, including during the hiring and onboarding process.
Click the following link for more information on your rights as an Applicant in the United States. http://www.capgemini.com/resources/equal\-employment\-opportunity\-is\-the\-law
Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55\-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end\-to\-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.
Salary Context
This $94K-$191K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Capgemini, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($143K) sits 33% below the category median. Disclosed range: $94K to $191K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Capgemini AI Hiring
Capgemini has 20 open AI roles right now. They're hiring across AI Architect, Prompt Engineer, AI/ML Engineer, Data Engineer. Positions span Chicago, IL, US, Alpharetta, GA, US, New York, NY, US. Compensation range: $65K - $191K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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