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Data AI Consulting Director
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 Senior Director, AI Automotive and Manufacturing Solutions Product Leader to join our team!
Position Summary
We are seeking a Senior Director, AI Automotive and Manufacturing Solutions Product Leader to shape, build, and scale a portfolio of AI\-powered Service\-as\-Software solutions for the automotive and manufacturing industry.
This role reports to the Executive, AI Solutions Product Leader and leads the industry product agenda within the broader AI Solutions Platform portfolio. The leader will translate industry needs into repeatable AI product concepts, guide product execution, support commercialization, and partner closely with Forward Deployed Engineering, Product Engineering, industry, sales, delivery, and platform teams.
This is not a traditional product owner or consulting delivery role. It is a senior product leadership role focused on moving from bespoke services to reusable, platform\-led AI products. Final investment decisions, enterprise business ownership, and end\-to\-end commercial accountability remain with executive governance and business leadership.
Role Mandate
- Shape the product strategy and roadmap for AI Automotive and Manufacturing Solutions.
- Identify high\-value use cases across vehicle engineering and development, manufacturing operations, supply chain and planning, quality and compliance, connected vehicle platforms, aftermarket services, and service operations.
- Guide MVP definition, product build, launch readiness, adoption, and continuous improvement.
- Ensure industry products leverage shared AI platform capabilities and the 70% standardized / 30% configurable model.
- Support commercialization through product narratives, solution packaging, sales enablement, and strategic pursuit support.
- Convert market and client learning into reusable product patterns, accelerators, and roadmap recommendations.
Key Responsibilities
1\. Shape Industry Product Strategy
- Develop and refine the product strategy for AI Automotive and Manufacturing Solutions, aligned to the broader AI Solutions Platform and Service\-as\-Software strategy.
- Assess client needs, market demand, competitive trends, regulatory considerations, and industry shifts such as software\-defined vehicles, electrification, smart factory, supply chain resilience, quality automation, connected mobility, and aftermarket service transformation.
- Recommend roadmap priorities, product sequencing, business cases, and investment trade\-offs for review by the Executive, AI Solutions Product Leader and senior governance forums.
- Define where capabilities should be standardized through the shared AI platform versus configured for industry\-specific needs.
2\. Lead Product Execution
- Own day\-to\-day product leadership for the industry portfolio, from idea shaping and MVP definition through launch readiness and ongoing improvement.
- Define and prioritize roadmaps, epics, features, release plans, and product maturity milestones.
- Partner with engineering, AI/ML, architecture, cloud, security, delivery, and FDE teams to translate product priorities into executable build plans.
- Ensure products meet enterprise expectations for security, compliance, reliability, explainability, usability, and operational readiness.
3\. Build Reusable, Platform\-Led Solutions
- Design products using the 70% standardized / 30% configurable model to improve repeatability, scalability, speed to value, and margin potential.
- Ensure solutions can integrate with MES, PLM, ERP, supply chain platforms, shop floor systems, connected vehicle platforms, edge systems, and enterprise applications.
- Identify common client patterns that should become reusable workflows, agent capabilities, templates, data models, integrations, or platform services.
- Reduce bespoke delivery by working with platform, FDE, and engineering teams to industrialize repeatable assets.
4\. Support Commercialization and Market Activation
- Support GTM, sales, client executives, industry leaders, finance, and marketing in product positioning and market activation.
- Contribute to packaging, pricing inputs, value propositions, sales enablement, demos, product narratives, and executive client materials.
- Support strategic pursuits where the industry AI product portfolio is central to the opportunity.
- Help connect product capabilities to pipeline creation, TCV contribution, revenue realization, and margin improvement while overall commercial ownership remains with business and sales leadership.
5\. Build Cross\-Functional Alignment
- Serve as the industry product connector across AI strategy, platform product, industry teams, FDE, product engineering, delivery, sales, finance, marketing, and client leadership.
- Create alignment around roadmap priorities, product standards, launch readiness, client adoption, and commercialization plans.
- Resolve working\-level trade\-offs where possible and escalate strategic decisions through appropriate executive governance.
- Represent the industry product portfolio in product reviews, senior leadership discussions, and strategic client conversations.
6\. Track Product Performance
- Define and monitor product metrics such as adoption, usage, client outcomes, platform reuse, deployment repeatability, time to value, product maturity, launch readiness, pipeline influence, and stakeholder feedback.
- Use performance data and field learning to improve the product roadmap, support executive recommendations, and strengthen commercialization readiness.
Required Qualifications
- 12\+ years of experience in automotive, manufacturing, supply chain, product engineering, Industry 4\.0, connected mobility, or enterprise solution development.
- 10\+ years of deep domain expertise across vehicle engineering and development, manufacturing operations, supply chain and planning, quality and compliance, connected vehicle platforms, aftermarket services, and service operations.
- Over 5 years of leading product strategy, roadmap development, MVP definition, launch readiness, and product lifecycle execution.
- Over 5 years of experience working with AI, GenAI, agentic workflows, intelligent automation, data platforms, cloud ecosystems, model lifecycle management, integration architecture, and enterprise software delivery.
- Over 5 years of experience working to build fact\-based recommendations on product strategy, roadmap trade\-offs, business cases, and commercialization choices.
- Bachelor degree in business, technology, engineering, finance, insurance, manufacturing, supply chain, or a related field.
Preferred Qualifications
- Experience building or scaling AI, GenAI, agentic, automation, or data platform products in automotive and manufacturing environments.
- Familiarity with relevant industry platforms such as SAP S/4HANA, Siemens Teamcenter or Opcenter, PTC Windchill or ThingWorx, Dassault Systemes 3DEXPERIENCE, Oracle SCM, Kinaxis, Blue Yonder, AUTOSAR, connected vehicle or telematics platforms, or equivalent.
- Experience with Service\-as\-Software, productized services, platform businesses, or reusable solution models.
- Experience moving from bespoke solution delivery toward repeatable, product\-led delivery models.
- MBA or advanced degree in business, engineering, computer science, data science, or a related field.
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.
NTT DATA provides a reasonable range of compensation for U.S.\-based positions. The starting pay range for this remote role is $201,015\.00\-$372,250\.00\. 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 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.
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 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 $201K-$372K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($286K) sits 31% above the category median. Disclosed range: $201K to $372K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
NTT DATA AI Hiring
NTT DATA has 14 open AI roles right now. They're hiring across AI Consultant, AI/ML Engineer, AI Architect, Prompt Engineer. Positions span Plano, TX, US, Atlanta, GA, US, Irving, TX, US. Compensation range: $114K - $426K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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