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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.
General Information
Job Description:
We are seeking a highly accomplished Enterprise Architect with deep expertise in Machine Learning, Agentic AI, and Enterprise AI Transformation to drive the organization's AI strategy and architecture vision. This leadership role is responsible for defining and governing enterprise-wide AI architecture, accelerating industrialization of AI capabilities, and enabling business transformation through scalable, secure, and reusable AI platforms and assets.
The Enterprise Architect will act as a trusted advisor to senior leadership, business stakeholders, and technology teams, ensuring alignment between strategic business objectives and AI investments. The role requires a combination of strategic thinking, technical leadership, innovation, architecture governance, and delivery oversight to enable enterprise-scale adoption of AI solutions.
Location Requirement: New York City. Candidates must be able to work onsite at the NYC office four (4) days per week . Key Responsibilities:
- Define and drive the enterprise AI architecture strategy, ensuring alignment with organizational objectives, digital transformation initiatives, and technology roadmaps.
- Establish enterprise architecture standards, governance frameworks, and best practices for AI, ML, and Generative AI solutions.
- Lead the design and evolution of scalable, secure, reusable, and future-ready AI platforms, frameworks, and accelerators.
- Drive the industrialization and adoption of AI assets across business units, ensuring maximum reuse and organizational value.
- Provide executive-level technology leadership and architectural guidance to business leaders, delivery organizations, and client stakeholders.
- Lead enterprise-wide AI transformation initiatives, identifying opportunities to leverage AI for business growth, operational efficiency, and innovation.
- Establish reference architectures, design patterns, and reusable frameworks for AI and Gen AI solutions.
- Drive architecture reviews and ensure compliance with enterprise standards for security, privacy, risk management, and responsible AI.
- Collaborate with business stakeholders to define AI strategies, roadmap priorities, investment plans, and measurable business outcomes.
- Lead architecture governance boards and ensure consistency across programs, products, and platforms.
- Evaluate emerging technologies, AI frameworks, foundation models, and industry trends to define future-state architecture.
- Drive cloud-native AI adoption leveraging Azure, AWS, GCP, and modern platform engineering practices.
- Oversee enterprise AI platform deployment strategies, including MLOps, LLMOps, CI/CD, observability, monitoring, and operational excellence.
- Define data architecture principles and integration patterns supporting AI-driven business capabilities.
- Mentor and develop architects, AI leaders, and engineering teams, fostering a culture of innovation, engineering excellence, and continuous learning.
- Partner with delivery and product teams to ensure successful implementation of architecture vision across strategic initiatives.
- Drive enterprise adoption of responsible AI practices, ensuring compliance with regulatory, security, privacy, and ethical requirements.
- Influence technology investment decisions and support development of business cases for strategic AI initiatives.
- Represent the organization in client engagements, executive architecture discussions, innovation forums, and industry thought leadership initiatives.
Qualifications & Skills:
- + Master’s degree in computer science, Engineering, Artificial Intelligence, Data Science, Information Systems, or a related discipline. + Ph.D. in AI, Machine Learning, Computer Science, or related field is desirable. + 20+ years of overall IT experience with strong expertise in Enterprise Architecture and AI led Digital Transformation. + Proven experience defining enterprise technology strategy and architecture governance frameworks. + Deep expertise in enterprise GenAI and Agentic AI architecture patterns including multi-agent systems, reasoning frameworks, orchestration architectures, RAG, knowledge engineering, AI platform design, model ecosystems, and LLM integration strategies. + Lead definition and evolution of enterprise AI platforms, shared services, reusable agents, accelerators, and AI operating modelsDefine enterprise AI operating model, governance structures, decision-making processes, and adoption framework supporting scalable AI transformation.
+ Demonstrated success in delivering and scaling Gen AI/Agentic AI, Machine Learning, and Generative AI solutions in large enterprise environments. + Experience leading architecture teams and influencing senior executives, clients, and cross-functional organizations. + Strong background in enterprise modernization, cloud transformation, platform engineering, and AI industrialization. + Deep expertise in Generative or Agentic AI ecosystems including OpenAI, Azure OpenAI, aws Bedrock, gcp adk, Claude, Gemini, Llama, LangChain, langraph , Agentic AI frameworks, and Retrieval-Augmented Generation (RAG) architectures. + Define enterprise standards and architecture patterns for Agentic AI including multi-agent orchestration, memory frameworks, tool integration, human oversight, evaluation frameworks, observability, and lifecycle management. + Strong experience with MLOps, LLMOps, AI governance, model lifecycle management, monitoring, and observability. + Familiarity with cybersecurity, regulatory compliance, data privacy, responsible AI, and risk management frameworks. Leadership & Professional Skills
- Exceptional stakeholder management and executive communication skills.
- Strong strategic thinking and business acumen with the ability to align technology investments to business outcomes.
- Proven ability to influence organizational decisions and drive enterprise-wide technology adoption.
- Strong leadership, mentoring, and talent development capabilities.
- Excellent problem-solving, decision-making, and analytical skills.
- Ability to operate effectively in complex, global, and matrix-based organizations.
- Strong customer-centric mindset with experience supporting client-facing engagements and executive presentations.
The base compensation range for this role in the posted location is: $167,500 - $245,200 USD
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 $167K-$245K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $167K to $245K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Capgemini AI Hiring
Capgemini has 42 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer, AI Agent Developer. Positions span Philadelphia, PA, US, Brooklyn, NY, US, Waltham, MA, US. Compensation range: $85K - $400K.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 tracked positions. That's 4% 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 Safety roles lead at $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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