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 of Data AI to join our team in Plano, Texas (US-TX), United States (US).
Position Summary
We are seeking an Managing Director, Data AI to shape and accelerate NTT DATA’s AI business across the Consumer Services and CPG industries.
This is a pivotal market-facing leadership position responsible for defining and executing the AI go-to-market strategy aligned with NTT DATA’s business priorities, industry strategy and customer needs. The leader will drive growth across NTT DATA’s AI services, solutions and capabilities by creating demand, shaping high-value opportunities, developing strategic partnerships and converting AI innovation into measurable commercial outcomes.
The role will operate at the intersection of industry, AI consulting, solution architecture, sales, partnerships and delivery, helping clients move from AI ambition and experimentation to scalable enterprise adoption.
The Managing Director will:
- Serve as an AI evangelist with clients, partners and the broader ecosystem, positioning NTT DATA around the future of applied AI and agentic intelligence in enterprise environments.
- Define and execute the AI GTM strategy for Consumer Services and CPG.
- Accelerate AI revenue growth, pipeline creation and opportunity conversion.
- Identify high-value client opportunities and shape monetizable AI use cases.
- Orchestrate AI consulting, architecture and implementation opportunities across clients and prospects.
- Develop repeatable and scalable GTM models that translate AI innovation into business value.
- Lead strategic AI partnerships and establish joint GTM initiatives with key ecosystem partners.
- Partner with sales, industry, consulting, architecture, delivery and marketing organizations to convert market demand into revenue.
- Serve as an external AI evangelist with customers, partners and the broader ecosystem.
Accountabilities
Define and Execute the Industry AI GTM Strategy
- Define the AI GTM strategy for Consumer Services and CPG aligned to NTT DATA’s broader AI strategy and business priorities.
- Translate market trends, client needs and emerging AI capabilities into differentiated industry propositions and solutions.
- Identify priority market segments, accounts, use cases and opportunities where NTT DATA can establish differentiated AI positioning.
- Develop repeatable commercial plays that accelerate adoption across Retail, Hospitality, Logistics and CPG.
- Ensure GTM priorities translate into clear commercial actions across sales, consulting, architecture, partnerships and delivery.
Drive AI Revenue Growth
- Own and drive AI revenue growth and associated GTM efforts across the Consumer Services and CPG portfolio.
- Partner with commercial and industry sales teams to generate, qualify, shape and convert AI demand.
- Create executive-level client engagement strategies that uncover high-value AI transformation opportunities.
- Help move opportunities from ideation and consulting through implementation and scaled adoption.
- Drive measurable direct and downstream pipeline and revenue impact.
Lead AI Consulting Solution Shaping
- Orchestrate and enable strategic AI consulting and architecture engagements across the client and prospect base.
- Engage directly with senior client stakeholders to understand business priorities and identify where AI can create measurable value.
- Shape high-value, monetizable AI use cases with clear business outcomes and routes to implementation.
- Bring together consulting, architecture, product, engineering and delivery expertise to develop compelling client solutions.
- Program manage strategic AI consulting and implementation engagements across North America.
- Use initial consulting and advisory engagements to establish pathways to larger downstream AI transformation programs.
Build Strategic AI Partnerships
- Lead key AI partnerships relevant to Consumer Services and CPG.
- Develop joint value propositions and GTM strategies with strategic technology and ecosystem partners.
- Partner with the commercial organization to activate co-sell and partner-led opportunities.
- Identify emerging AI platforms, technologies and ecosystem relationships that can enhance NTT DATA’s market position.
- Create joint market plays that expand access to customers and accelerate revenue opportunities.
Operationalize Scalable AI GTM Models
- Identify high-value opportunities and convert them into repeatable industry use cases, solutions and GTM plays.
- Develop mechanisms to accelerate the translation of opportunities into commercial pursuits.
- Establish scalable GTM models that reduce one-off approaches and improve repeatability.
- Connect insights from clients and the marketplace back into NTT DATA’s AI portfolio and solution strategy.
- Work across organizational boundaries to ensure promising AI concepts can progress from innovation into enterprise-scale adoption.
Establish Market Leadership
- Serve as an external evangelist for NTT DATA’s AI capabilities with customers, partners and the broader ecosystem.
- Clearly articulate NTT DATA’s AI value proposition to senior enterprise clients.
- Partner with Marketing to generate market awareness and strengthen NTT DATA’s position in AI solutions.
- Represent NTT DATA in strategic client forums, industry discussions and partner engagements.
- Build market credibility around applied AI, generative AI and agentic intelligence within Consumer Services and CPG.
Lead Cross-Functional GTM Execution
- Work across Sales, Industry, Consulting, Architecture, Product, Engineering, Delivery, Alliances and Marketing to coordinate AI GTM execution.
- Create alignment around priority opportunities, clients, partners and industry propositions.
- Remove barriers that slow opportunity progression and client adoption.
- Establish clear accountability for moving qualified AI opportunities through the commercial lifecycle.
- Operate effectively within NTT DATA’s global and regional matrix while maintaining focus on measurable commercial results.
Required Qualifications
- 15+ years of experience across AI, data, technology services, consulting, industry solutions, digital transformation, GTM or related leadership disciplines.
- Significant experience building and executing technology or AI go-to-market strategies.
- Strong industry experience within Consumer Services and/or CPG, ideally including Retail, Hospitality, Logistics or adjacent sectors.
- Proven ability to identify, shape and convert complex enterprise technology opportunities.
- Strong knowledge of AI, generative AI, applied intelligence and emerging agentic AI models.
- Experience shaping enterprise AI use cases and translating technical capabilities into measurable business outcomes.
- Demonstrated commercial orientation with experience influencing pipeline and revenue growth.
- Experience working across sales, consulting, architecture, engineering, delivery, marketing and alliance organizations.
- Strong client-facing capability with credibility engaging senior business and technology executives.
- Experience operating within a complex global and matrixed organization.
- Ability to translate emerging technologies into compelling industry-specific business narratives.
Preferred Qualifications
- Experience leading AI, Data AI, GenAI or digital transformation GTM within a global technology services, consulting or systems integration organization.
- Deep industry relationships within Consumer Services or CPG.
- Experience developing AI propositions, solutions or industry-specific use cases.
- Experience establishing strategic AI technology partnerships and joint GTM models.
- Experience moving AI pilots and consulting engagements into enterprise-scale implementation programs.
- Demonstrated thought leadership within AI and emerging technology.
Core Competencies
- AI go-to-market leadership
- Consumer Services CPG industry expertise
- Commercial and revenue leadership
- Executive client engagement
- AI consulting and solution shaping
- Applied and agentic AI
- Strategic partnership development
- Demand generation and conversion
- Pipeline development
- Cross-functional leadership
- Market positioning and evangelism
- Strategic influence
- Enterprise stakeholder management
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: $178K across 1623 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 9,079 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 $211,950 based on 13,048 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 $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
NTT DATA AI Hiring
NTT DATA has 14 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Positions span Plano, TX, US, TX, US, Charlotte, NC, US. Compensation range: $125K - $372K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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