Interested in this AI/ML Engineer role at NTT DATA?
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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 Managing Director, Global AI Automotive Leader to join our team in Plano, Texas (US-TX), United States (US).
Position Summary
We are seeking a Managing Director, Global AI Automotive Leader to shape and execute NTT DATA’s global AI growth agenda for the Automotive portfolio. This senior executive will lead the AI solutions and offerings development, AI-led transformation plays, ecosystem partnerships and alliances, and strategic initiatives designed to accelerate growth across priority Automotive markets.
As a leading partner for AI-enabled enterprise transformation in Automotive, the leader will elevate NTT DATA’s relevance in the market, create differentiated and industry-specific AI value propositions, and accelerate adoption of verticalized AI solutions across the global Automotive landscape. The role will operate at the intersection of Automotive industry strategy, AI, consulting, digital engineering, cloud, data, cybersecurity, infrastructure, managed services and commercial execution, bringing together NTT DATA’s capabilities into a compelling and differentiated market proposition.
The Executive will also steward NTT DATA’s Automotive AI narrative, thought leadership agenda and industry influence, building credibility with C-suite leaders shaping the future of the automotive industry.
Accountabilities
Define Execute the Global Automotive AI GTM Strategy
- Define and drive the global GTM strategy for Automotive, aligned with enterprise growth priorities, AI innovation themes and market opportunities.
- Identify priority markets, clients, transformation themes and AI opportunities where NTT DATA can build differentiated positioning.
- Establish segmented GTM plays for Tier 1 global automotive manufacturers.
- Translate global Automotive and AI market dynamics into actionable commercial strategies.
- Develop repeatable GTM models that can be activated across regions while accounting for local market and client requirements.
- Partner with regional leadership to embed global strategy into local execution.
Build a Differentiated Automotive AI Value Proposition
- Shape and operationalize a unified global Automotive value proposition that clearly articulates how NTT DATA wins in the market.
- Bring together consulting, digital engineering, cloud, data AI, cybersecurity, infrastructure and managed services into integrated Automotive propositions.
- Develop industry-specific AI solutions and transformation plays that address the strategic priorities of global Automotive organizations.
- Identify high-value opportunities and translate AI innovation into monetizable client propositions.
- Create a portfolio of repeatable and scalable AI plays that strengthens differentiation and accelerates client adoption.
Drive AI-Led Automotive Transformation
- Embed AI-first thinking into Automotive GTM strategies, client conversations and solution development.
- Develop AI-led transformation plays, generative AI opportunity maps and industry AI adoption accelerators.
- Identify emerging use cases where applied AI and agentic AI can create measurable client value.
- Engage senior Automotive clients around enterprise AI strategy, transformation priorities and adoption opportunities.
- Connect client demand with NTT DATA’s AI, consulting, engineering, product and delivery capabilities.
- Help move strategic AI opportunities from concept and advisory through implementation and scaled adoption.
Accelerate Global Growth Commercial Execution
- Align global Automotive AI strategy with measurable growth and commercial objectives.
- Identify and shape priority opportunities across strategic Automotive accounts and markets.
- Partner with regional PLs and commercial leaders to build and convert AI-related demand.
- Provide executive sponsorship and industry expertise for strategic pursuits and complex opportunities.
- Establish mechanisms for sharing successful propositions, use cases and commercial plays across markets.
- Drive greater consistency and repeatability in how NTT DATA takes Automotive AI solutions to market globally.
Build Strategic Ecosystem Partnerships Alliances
- Identify and develop strategic ecosystem partnerships that strengthen NTT DATA’s Automotive AI capabilities and market position.
- Create joint value propositions and GTM motions with priority technology and industry partners.
- Connect alliance strategy with priority Automotive transformation themes and client opportunities.
- Activate partner relationships to accelerate market access, differentiation, innovation and commercial opportunity.
- Monitor emerging AI and Automotive ecosystem developments and identify opportunities to strengthen NTT DATA’s portfolio.
Establish NTT DATA as an Automotive AI Market Leader
- Steward NTT DATA’s global Automotive AI narrative and external market positioning.
- Serve as a senior external evangelist with Automotive clients, partners and the broader industry ecosystem.
- Build executive relationships with C-suite leaders shaping the future of the Automotive industry.
- Partner with global Marketing to strengthen brand relevance, market awareness and industry credibility.
- Develop and contribute to thought leadership around AI-led Automotive transformation.
- Represent NTT DATA in strategic customer forums, partner engagements and relevant industry conversations.
- Translate emerging AI trends into compelling Automotive business narratives and commercial opportunities.
Orchestrate the Enterprise Around Automotive AI
- Serve as the enterprise point connecting Automotive AI strategy across regional PLs, service lines, consulting, AI Centers of Excellence, product groups and global marketing.
- Create alignment around priority clients, markets, propositions, investments and GTM plays.
- Enable regional organizations to execute the global strategy with appropriate market flexibility.
- Facilitate reuse of capabilities, propositions, accelerators and industry knowledge across geographies.
- Establish strong collaboration across commercial, consulting, technology, delivery, alliance and marketing organizations.
- Influence across organizational boundaries to create a cohesive and differentiated experience for global Automotive clients.
Required Qualifications
- 15+ years of leadership experience across Automotive, technology services, AI, digital transformation, consulting, engineering or related disciplines.
- Deep knowledge of the global Automotive industry and its evolving technology and transformation priorities.
- Demonstrated experience defining and executing global or multi-market GTM strategies.
- Strong understanding of AI, generative AI, applied intelligence and emerging agentic AI capabilities.
- Proven ability to translate emerging technology into industry-specific transformation propositions and measurable business outcomes.
- Experience developing and commercializing industry solutions, offerings or transformation plays.
- Strong commercial orientation with experience shaping significant and complex enterprise opportunities.
- Experience working across consulting, technology, engineering, sales, delivery, product, alliances and marketing organizations.
- Demonstrated ability to influence senior business and technology executives within Automotive organizations.
- Experience operating successfully within large, global and highly matrixed organizations.
- Executive presence with the ability to represent the organization with clients, partners and the broader industry ecosystem.
Preferred Qualifications
- Leadership experience within a global systems integrator, technology services company, consulting organization, enterprise software organization or major Automotive company.
- Experience developing AI-led solutions or transformation strategies specifically for Automotive clients.
- Strong executive relationships across global Automotive manufacturers and the broader Automotive ecosystem.
- Experience developing strategic technology partnerships and joint GTM propositions.
- Experience taking new AI or technology propositions from concept through commercialization and scale.
- Demonstrated external thought leadership within Automotive, AI, digital engineering or emerging technology.
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.
Salary Context
This $150K-$350K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2094 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 10,827 AI roles we're tracking, AI/ML Engineer positions make up 77% 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 $212,160 based on 13,809 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($250K) sits 18% above the category median. Disclosed range: $150K to $350K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. 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: $272,300; VP: $236,450.
NTT DATA AI Hiring
NTT DATA has 19 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Positions span Chicago, IL, US, Irving, TX, US, Atlanta, GA, US. Compensation range: $131K - $372K.
Location Context
Across all AI roles, 6% (665 positions) offer remote work, while 10,097 require on-site attendance. Top AI hiring metros: New York (3,262 roles, $221,000 median); San Francisco (2,767 roles, $260,600 median); Los Angeles (1,585 roles, $210,000 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 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. 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 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). 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 (122) are outnumbered by mid-level (5,256) and senior (3,922) 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 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 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 $264,111, 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 (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 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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