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About This Role
Req ID: 382336
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 AI Training Sr. Consultant \- Developer End User Enablement to join our team in Remote, Texas (US\-TX), United States (US).
AI Training Sr. Consultant – Developer End User Enablement
Position Summary
NTT DATA is seeking an experienced AI Trainer to design, develop, and deliver a comprehensive AI learning program for both software developers and end users. This role combines expertise in instructional design, technical training delivery, and modern software development practices to help accelerate AI adoption across the enterprise.
The ideal candidate brings hands\-on experience with the Software Development Lifecycle (SDLC), AI\-assisted development tools, and enterprise training programs. This individual will partner with leading AI technology providers, including Microsoft, GitHub, OpenAI, Anthropic, and others, to develop practical, role\-based curriculum that enables developers and business users to effectively leverage AI technologies within NTT DATA's environment.
Key Responsibilities
AI Curriculum Development
- Design and maintain a comprehensive AI learning curriculum for developers, technical teams, and business users.
- Develop instructor\-led training, workshops, labs, playbooks, learning guides, and hands\-on exercises.
- Create role\-based learning pathways ranging from AI literacy through advanced AI engineering and AI\-assisted software development.
- Continuously update training content to reflect evolving AI platforms, tools, and industry best practices.
Technical Training Delivery
- Deliver engaging instructor\-led training sessions, workshops, office hours, webinars, and hands\-on labs.
- Train software engineers on AI\-powered development tools, including code generation, agentic workflows, testing automation, and developer productivity solutions.
- Facilitate practical exercises that demonstrate real\-world application of AI within enterprise software development environments.
- Adapt delivery approaches for technical and non\-technical audiences.
Developer Enablement
- Build training programs focused on modern software development practices and AI\-enabled SDLC processes.
- Educate development teams on tools and platforms from Microsoft, GitHub, OpenAI, Anthropic, and other AI providers.
- Create learning experiences focused on responsible AI, prompt engineering, AI\-assisted coding, AI agents, and software engineering productivity.
- Enable developers to integrate AI capabilities into business applications while maintaining security, quality, and governance standards.
Stakeholder Collaboration
- Partner with technology leaders, architecture teams, development managers, and AI program leaders to identify capability gaps and training needs.
- Collaborate with internal subject matter experts and external technology partners to ensure training remains technically accurate and relevant.
- Align curriculum and training experiences with NTT DATA's technology standards, business priorities, and AI strategy.
Program Evaluation Continuous Improvement
- Measure learner engagement, adoption, and proficiency improvements.
- Gather feedback and performance metrics to continuously improve learning effectiveness.
- Track emerging AI technologies and recommend updates to training programs and learning pathways.
Required Qualifications
- Bachelor's degree in Computer Science, Information Technology, Learning Development, Instructional Design, or related field.
- 7\+ years of experience in technical training, developer enablement, learning program management, or technology consulting.
- 5\+ years of experience working within software development environments and SDLC processes.
- Demonstrated experience developing and delivering technical training for software engineers and technology professionals.
- Experience with AI and Generative AI technologies, including practical use cases and enterprise adoption.
- Strong understanding of Agile, DevOps, CI/CD, software engineering best practices, and application lifecycle management.
- Proven curriculum design and instructional development experience.
- Exceptional facilitation, presentation, communication, and stakeholder management skills.
- Ability to simplify complex technical concepts into practical learning experiences.
\#\# Preferred Qualifications
Hands\-on experience with:
GitHub Copilot
Microsoft AI and Azure AI services
OpenAI platforms and APIs
Anthropic Claude
AI agents and agentic workflows
Prompt engineering methodologies
Responsible AI frameworks and governance
Experience supporting enterprise AI transformation initiatives.
Software development background in one or more programming languages.
Experience developing hands\-on labs, technical workshops, and certification\-based learning programs.
Relevant certifications in Microsoft Azure, AI, GitHub, instructional design, or learning technologies.
\#\# Success Measures
The successful candidate will:
Establish a scalable AI learning curriculum for developers and business users.
Improve AI adoption and proficiency across NTT DATA's developer community.
Deliver high\-impact training that increases productivity through AI\-assisted development.
Create practical, NTT DATA\-specific learning experiences that enable safe, responsible, and effective AI usage.
Serve as a trusted advisor to business and technology teams on AI capability development and workforce enablement.
\#\#\# Ideal Candidate Profile
We are looking for a rare combination of:
Technical educator
Software development practitioner
AI enthusiast and thought leader
Curriculum designer
Enterprise change agent
This individual is equally comfortable discussing prompt engineering with developers, facilitating executive AI workshops, building hands\-on labs, and shaping a long\-term enterprise AI enablement strategy.
Where required by law, NTT DATA provides a reasonable range of compensation for specific roles. The starting pay range for this remote role is $87,000\.00\-$160,000\.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.
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 $87K-$160K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($123K) sits 43% below the category median. Disclosed range: $87K to $160K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
NTT DATA AI Hiring
NTT DATA has 13 open AI roles right now. They're hiring across AI/ML Engineer, LLM Engineer, AI Architect. Positions span Plano, TX, US, Dallas, TX, US, TX, US. Compensation range: $128K - $450K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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