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About This Role
Req ID: 392208
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Principal AI Domain Technical Leader
Role Summary
As a Principal AI Domain Technical Leader, you will be the senior technical authority for one of North America AI Delivery's vertical portfolios: Products, Insurance, Banking and Financial Services, or Healthcare, Pharma, Life Sciences and SLED. Reporting to the AI Delivery Executive for the vertical, with a dotted line to the North America AI Delivery Technical Lead, you will own the technical answer on the portfolio's most strategic pursuits and transformations, provide technical assurance across its engagements, and build the reusable patterns the vertical's next engagement starts from.
This is a billable technical leadership role. You will lead technically on two or three strategic client transformations at a time, standing in front of client CTOs, chief architects and engineering leaders alongside the AI Delivery Executive, and remaining accountable to the Engagement Leader for the engagement outcome. The balance of your time goes to pursuit shaping, architecture and engineering assurance, and the vertical's reference architectures, accelerators and deposits into the AI Studio Workflow Vault.
You will not manage pod resources. The North America Technical Lead owns personnel leadership, the hiring bar and technical career development for onshore technical staff; your role is to mentor the vertical's architects and engineers through the work, set the technical bar on its engagements, and surface capability gaps and performance concerns to the Technical Lead.
Key Responsibilities
- Own the technical solution on the vertical's strategic pursuits: feasibility, architecture, delivery model, staffing shape, dependencies, technical risk and assumptions, validated with Global Demand Solution Engineering before commitments are made.
- Lead technically on two or three strategic transformations at a time as a billable member of the engagement, at the level of architecture, integration and technical risk.
- Provide technical assurance across the vertical's engagements at the checkpoints that matter solution baseline, first production release, major scope change, and any engagement in difficulty; route findings through the Engagement Leader and AI Delivery Executive.
- Lead technical remediation on engagements when needed, with the NA Technical Lead and Global Technical or COE specialists.
- Own the vertical's reference architectures, reusable patterns and accelerators; deposit them in the Workflow Vault, keep them current and measure their adoption.
- Represent the AI Studio operating fabric, its five-layer model and its technical standards to client technology leaders in the industry's own terms, including its regulatory, data, security and responsible-AI context.
- Build and maintain the technical relationship with client CTOs, chief architects and engineering leaders alongside the AI Delivery Executive, before and outside escalations.
- Serve as the first escalation point for Engagement Leaders on technical feasibility and architecture; decide what needs the NA Technical Lead.
- Mentor the vertical's architects, engineers and data scientists; surface capability gaps and performance concerns to the NA Technical Lead.
- Carry industry-specific requirements into the Global AI Studio standards conversation; apply the global standard in the vertical without creating variants.
Basic Qualifications
- Bachelor's degree in a technical discipline or equivalent professional experience.
- 12+ years in enterprise technology delivery, with 5+ years leading architecture or engineering on AI, data or platform transformations that reached production.
- 5+ years across cloud, data, and AI architecture, with recent hands-on exposure to GenAI/agentic architectures, MLOps/LLMOps, security, and responsible AI.
- 5+ years experience mentoring senior technical staff in a matrixed, globally distributed delivery organization.
- Deep, current knowledge of the vertical: its systems of record, data landscape, regulatory and security requirements, and the technology partners that matter in it.
- Demonstrated ownership of the technical solution on major pursuits: feasibility, architecture, estimation and technical risk, working alongside sales and delivery leadership.
- Experience providing architecture and engineering assurance across multiple concurrent engagements, and leading technical recovery on at least one engagement in difficulty.
- Track record of creating reusable patterns, reference architectures or accelerators that other teams adopted.
- Credibility with client CTOs, chief architects and engineering leaders; able to explain a modern AI operating model in the industry's language.
- Excellent written and verbal communication; able to say no to an unsound commitment with a better option attached.
Preferred Qualifications
- Hands-on experience with agentic AI, workflow orchestration, model gateways and evaluation frameworks in production.
- Experience with outcome-based or managed-service delivery constructs where the technical solution carries commercial risk.
- Contribution to a technical community or practice: standards, training, communities of practice, published patterns.
- Familiarity with the AI Studio operating fabric, the Workflow Accelerator and the Workflow Vault, or comparable platform-based delivery models.
- Experience across more than one of the target verticals.
- Relevant architecture or cloud certifications (TOGAF, Azure, AWS, Google Cloud) or equivalent.
NTT DATA provides a reasonable range of compensation for U.S.-based positions. The starting pay range for this role will depend on the nature of the role offered and could be expected to fall within a range of $185,900 - $309,000. Actual compensation will depend on a number of factors, including the candidate’s relevant experience, technical skills, and other qualifications.
This position may also be eligible for incentive compensation based on individual and/or company performance. If the position offered in temporary, the position will not be eligible for incentive compensation.
This position is eligible for company benefits that will depend on the nature of the role offered. Company benefits may include medical, dental, and vision insurance, 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.
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 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 $185K-$309K 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 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($247K) sits 17% above the category median. Disclosed range: $185K to $309K.
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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