Interested in this AI/ML Engineer role at NTT DATA?
Apply Now →Skills & Technologies
About This Role
Make an impact with NTT DATA
Join a company that is pushing the boundaries of what is possible. We are renowned for our technical excellence and leading innovations, and for making a difference to our clients and society. Our workplace embraces diversity and inclusion – it’s a place where you can grow, belong and thrive.
The Generative AI Solutions Architect designs, develops, and supports AI-enabled applications, workflows, and reusable capabilities while helping teams evaluate and implement AI solutions effectively. This role blends software engineering, AI architecture, and stakeholder consulting to drive secure, scalable, and sustainable AI adoption across the GIN organization.
Want to be a part of our team?
This is a role in our Global IP Network (GIN) division. NTT and the Global IP Network have been recognized as the Best Global Wholesale Carrier (Data) 2021 at the Global Carrier Awards. Our Tier-1 Global IP Network, consistently ranked among the top networks worldwide, spans the Americas, Europe, Asia and Oceania, operating under a single AS 2914.
Key Responsibilities:
- Design, develop, test, deploy, and maintain GenAI-enabled applications, services, APIs, and reusable components for internal platforms and project teams.
- Evaluate and select foundation models and deployment approaches, including open-source and locally hosted models such as Qwen and Ollama-based deployments, using quality, latency, cost, security, privacy, and platform-fit criteria.
- Design and integrate model-driven workflows such as prompting, retrieval-augmented generation (RAG), tool use, and agentic patterns, with appropriate testing, evaluation, guardrails, and fallback behavior.
- Provide early technical input to project teams on feasibility, architecture, model selection, data requirements, GPU capacity, security, responsible AI considerations, performance, and operational risks.
- Create and maintain reference designs, technical documentation, evaluation methods, operating procedures, standards, observability guidance, and recommended usage patterns for GenAI solutions.
- Partner with software, infrastructure, platform, security, and operations teams; educate technical and non-technical stakeholders on GenAI capabilities and limitations; and provide actionable feedback to the Executive, Head - SAS on platform gaps and improvement priorities.
- Develop and maintain reporting at established intervals on significant GIN AI initiatives, activities, and project outcomes. Maintain current GIN AI documentation and guidance, including recommended AI models, core tools, platforms, and other resources available to GIN staff, updating content as organizational needs, technologies, and standards evolve.
Education / Qualifications required:
- Bachelors degree in Computer Science, Software Engineering, Information Systems, or a related field; or equivalent practical experience.
- Working knowledge of software engineering practices relevant to GenAI solutions, including Python or comparable programming, APIs, source control, testing, containers, and automated deployment practices.
- Working knowledge of GPU compute and memory considerations, foundation-model capabilities and limitations, and open-source or locally hosted model platforms, including tools such as Ollama and models such as Qwen.
Work Experience Required:
- Demonstrated experience designing, building, and supporting GenAI-enabled applications, services, or internal tools in an enterprise or applied engineering environment.
- Demonstrated experience working across software engineering, infrastructure, architecture, security, operations, and business stakeholders to translate use cases into practical technical solutions.
- Demonstrated experience developing and integrating model-driven workflows, including prompting, inference patterns, testing, evaluation, and support practices.
- Demonstrated experience creating technical documentation, implementation guidance, or standards for both technical and non-technical audiences.
- Demonstrated experience working in network services, telecommunications, managed services, or similar infrastructure-based companies.
Skills and Core Competencies:
- Ability to assess technical tradeoffs involving solution quality, cost, latency, performance, scalability, maintainability, security, privacy, and operational fit.
- Strong written and verbal communication skills, including the ability to explain complex GenAI concepts, risks, and recommendations clearly to mixed audiences.
- Strong analytical and experimental approach to model and workflow evaluation, including defining acceptance criteria and interpreting evaluation results.
- Ability to work independently and across project teams, exercise sound technical judgment, identify risks early, and recommend approaches that can be supported at scale.
Organizational Relationships:
- Reports directly to the Executive Head, SAS
- Works closely with software engineering, infrastructure, platform, security, and operations teams.
- Engages with technical leadership, project teams, and non-technical stakeholders on a project basis.
Success Measures:
- Delivery of reliable, supportable GenAI tools, services, or reference implementations that meet defined acceptance criteria and are adopted by project teams.
- Build and maintain clear and current documentation, standards, and operating guidance that support consistent implementation and support practices.
- Early identification of GenAI design issues, security or responsible AI risk, resource constraints, and operational gaps with actionable recommendation that improve AI platform readiness.
- Demonstrable improvements in solution quality, latency, cost, reliability, or supportability through effective evaluation, architecture, and operational practices.
NTT DATA provides a reasonable range of compensation for U.S.-based positions. The starting pay range for this remote role is 92K-131K. 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.
Workplace type: Remote Working
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 70 countries. We also offer clients access to a robust ecosystem of innovation centers as well as
established and start-up partners. NTT DATA is part of NTT Group, which invests over $3 billion each
year in RD.
Equal Opportunity Employer
NTT DATA is proud to be an Equal Opportunity Employer with a global culture that embraces diversity. We are committed to providing an environment free of unfair discrimination and harassment. We do not discriminate based on age, race, colour, gender, sexual orientation, religion, nationality, disability, pregnancy, marital status, veteran status, or any other protected category. Join our growing global team and accelerate your career with us. Apply today.
Third parties fraudulently posing as NTT DATA recruiters
NTT DATA recruiters will never ask job seekers or candidates for payment or banking information during the recruitment process, for any reason. Please remain vigilant of third parties who may attempt to impersonate NTT DATA recruiters whether in writing or by phone in order to deceptively obtain personal data or money from you. All email communications from an NTT DATA recruiter will come from an nttdata.com email address. If you suspect any fraudulent activity, please contact us.
Salary Context
This $92K-$131K range is in the lower quartile 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($111K) sits 47% below the category median. Disclosed range: $92K to $131K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.