Interested in this AI/ML Engineer role at NTT DATA?
Apply Now →About This Role
NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.
We are currently seeking a Executive, AI Solutions Product Leader - REMOTE to join our team in Plano, Texas (US-TX), United States (US).
Role Overview
NTT DATA is building AI Service-as-Software as a new product-led growth engine and one of the company's key strategic bets. We are seeking an Executive, AI Solutions Product Leader to stand up dedicated executive product leadership for the AI Solutions Platform, three industry-focused AI products, and shared platform capabilities.
This role will shape and execute the AI Service-as-Software product agenda, directly driving a strategic bet in partnership with NTT DATA AI Vista. The leader will translate AI strategy into a scalable, repeatable product portfolio with clear roadmaps, strong product maturity, improved platform reuse, commercialization-ready offerings, and disciplined product operating practices.
This is not a traditional Product Owner or consulting delivery role. It is an executive product leadership role focused on moving NTT DATA from bespoke AI services toward repeatable, platform-led AI Native growth.
What You Will Lead
- AI Solutions Platform product strategy and roadmap, including reusable horizontal capabilities across industries.
- Three industry-focused AI products, led by Senior Director-level Product Owners, with shared product standards and operating discipline.
- Shared platform capabilities such as agent orchestration, knowledge grounding, workflow automation, data integration, model management, governance, and enterprise integration.
- Product packaging and commercialization support, working closely with AI Vista, AI Market Makers, global industry teams, regional sales, and AI delivery.
- A lean, high-impact product organization in a product job family that is new to NTT DATA and requires scarce AI product, industry, and platform leadership capabilities.
Key Responsibilities
Shape Product Portfolio Strategy
- Define and refine the product strategy for the AI Solutions Platform and three industry-focused AI products.
- Translate the AI Service-as-Software ambition into a clear portfolio roadmap, product maturity milestones, and execution priorities.
- Recommend portfolio sequencing, business cases, and investment priorities for executive review.
- Assess client needs, market demand, AI platform trends, competitive dynamics, regulatory considerations, and commercial relevance to inform product direction.
- Translate the AI Service-as-Software ambition into a clear portfolio roadmap, product maturity milestones, and execution priorities.
- Recommend portfolio sequencing, business cases, and investment priorities for executive review.
- Assess client needs, market demand, AI platform trends, competitive dynamics, regulatory considerations, and commercial relevance to inform product direction.
Lead Product Leaders
- Lead, coach, and develop Senior Director-level Product Owners responsible for industry products and shared platform capabilities.
- Set expectations for product strategy, discovery, roadmap quality, execution discipline, stakeholder alignment, and product performance.
- Build a consistent product operating model across the portfolio, including governance rhythms, roadmap standards, product reviews, metrics, and escalation paths.
- Develop product leadership capability in a job family that is strategically important and not historically common at NTT DATA.
Guide Platform Product Execution
- Guide the roadmap and execution of the horizontal AI Solutions Platform in partnership with Product Engineering, AI/ML, architecture, cloud, security, and delivery teams.
- Define reusable platform capabilities that can be leveraged across industries and client environments.
- Promote a 70% standardized / 30% configurable model to improve repeatability, scalability, deployment speed, and margin potential.
- Ensure products are designed for enterprise-grade security, reliability, usability, compliance, operational readiness, and integration into client environments.
Support Commercialization and Growth
- Improve product-led commercialization through packaging inputs, pricing inputs, sales enablement, product narratives, demos, and executive client positioning.
- Support strategic pursuits where AI Service-as-Software products are central to the opportunity.
- Partner with AI Vista, AI Market Makers, global industry leaders, regional sales, finance, marketing, delivery, and product engineering stakeholders.
- Help directly drive the $125M current-year AI Service-as-Software strategic bet while sales targets, delivery revenue ownership, and client delivery accountability remain with business, sales, and delivery leadership.
Convert Market Learning into Product Reuse
- Work with Forward Deployed Engineering, delivery teams, industry leaders, sales teams, and strategic clients to identify repeatable AI use cases.
- Determine which client-specific patterns should be recommended for productization as shared capabilities, templates, accelerators, or platform services.
- Ensure field insights and client adoption data inform roadmap updates, platform improvements, launch readiness, and industry product evolution.
Required Qualifications
- 15+ years of experience in enterprise product leadership, AI platforms, digital platforms, enterprise software, productized services, or cross-industry solution development.
- Experience leading senior product leaders or cross-functional product teams in complex, matrixed environments.
- Proven ability to shape and scale product portfolios, platform capabilities, or enterprise solution offerings.
- Strong understanding of AI, GenAI, agentic workflows, enterprise data platforms, cloud ecosystems, model lifecycle management, integration architecture, and enterprise software delivery.
- Experience working across product, engineering, architecture, sales, delivery, finance, marketing, and senior executive stakeholders.
- Ability to build fact-based recommendations on product strategy, investment priorities, roadmap trade-offs, and commercialization choices.
- Strong executive presence and ability to influence across a global organization.
- Experience supporting commercialization, sales enablement, strategic pursuits, and client-facing product positioning.
Preferred Qualifications
- Experience building or scaling AI, GenAI, agentic, automation, or data platform products.
- Experience in a global systems integration, consulting, enterprise technology, SaaS, cloud, or platform business.
- Experience with Service-as-Software, productized services, or platform-led delivery models.
- Experience moving from bespoke solution delivery toward reusable, repeatable product models.
- MBA or advanced degree in business, engineering, computer science, data science, or related field.
Core Competencies
- Executive Product leadership
- Product portfolio management
- AI platform strategy
- Platform and systems thinking
- Commerical support
- Cross-functional influence
- Market-backed prioritization
- Product operating model design
- Client and industry insight
- Execution discipline
- Strategic recommendation development
- Global stakeholder management
Success Measures
- Progress in scaling the AI Solutions Platform and three industry-focused AI products.
- Quality and maturity of product roadmaps, business cases, launch readiness, and investment recommendations.
- Adoption and reuse of shared platform capabilities across industry products and client environments.
- Reduction in bespoke delivery through repeatable product and platform patterns.
- Commercialization readiness, including demos, product narratives, packaging inputs, sales enablement, and strategic pursuit support.
- Strength of alignment across AI Solutions Product, Product Engineering, Forward Deployed Engineering, AI Vista, AI Market Makers, industry, sales, and delivery teams.
- Effectiveness of the senior product leaders reporting into the role.
Why Join Us
This is an opportunity to establish executive product leadership for a new AI Service-as-Software growth model at NTT DATA. You will lead a high-caliber product organization, shape a $125M current-year strategic bet expected to grow to $800M+ in three years, and help turn AI capabilities into repeatable, scalable, platform-led offerings for global clients.
You will not simply manage a product backlog. You will build the product discipline, operating model, and portfolio needed to scale AI Native products across industries.
About NTT DATA
NTT DATA is a $30 billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world's leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. our consulting and Industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 50 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is a part of NTT Group, which invests over $3 billion each year in RD.
Whenever possible, we hire locally to NTT DATA offices or client sites. This ensures we can provide timely and effective support tailored to each client’s needs. While many positions offer remote or hybrid work options, these arrangements are subject to change based on client requirements. For employees near an NTT DATA office or client site, in-office attendance may be required for meetings or events, depending on business needs. At NTT DATA, we are committed to staying flexible and meeting the evolving needs of both our clients and employees. NTT DATA recruiters will never ask for payment or banking information and will only use @nttdata.com, @nttdatafed.com and @talent.nttdataservices.com email addresses. If you are requested to provide payment or disclose banking information, please submit a contact us form, https://us.nttdata.com/en/contact-us.
NTT DATA provides a reasonable range of compensation for specific roles. The starting pay range for this role is $150,000 - $350,000 per year. This range reflects the minimum and maximum target compensation for the position across all US locations. Actual compensation will depend on a number of factors, including the candidate’s actual work location, relevant experience, technical skills, and other qualifications. This position may also be eligible for incentive compensation based on individual and/or company performance. This role may also be eligible for incentive compensation based on individual and/or company performance.
This position is eligible for company benefits including medical, dental, and vision insurance with an employer contribution, flexible spending or health savings account, life and ADD insurance, short and long term disability coverage, paid time off, employee assistance, participation in a 401k program with company match, and additional voluntary or legally-required benefits.
NTT DATA endeavors to make https://us.nttdata.com accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact us at https://us.nttdata.com/en/contact-us. This contact information is for accommodation requests only and cannot be used to inquire about the status of applications. NTT DATA is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. For our EEO Policy Statement, please click here. If you'd like more information on your EEO rights under the law, please click here. For Pay Transparency information, please click here.
#LI-SEV #LI-NA
Salary Context
This $150K-$350K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At NTT DATA, 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 in Demand for This Role
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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($250K) sits 18% above the category median. Disclosed range: $150K to $350K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
NTT DATA AI Hiring
NTT DATA has 7 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Positions span Plano, TX, US, London Area, United Kingdom, Atlanta, GA, US. Compensation range: $145K - $372K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.