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About This Role
Req ID: 375005
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 Solutions Architect for Healthcare Payer and Life Science to join our team in Plano, Texas (US\-TX), United States (US).
AI Solution Architect for Healthcare Payer Segment
About the Role
We are seeking an experienced AI Solutions Architect to design and deliver artificial intelligence, machine learning, and generative AI solutions for health plan clients. This role will partner with payer executives, data leaders, and engineering teams to translate business challenges into scalable AI solutions.
The ideal candidate has deep knowledge of health plan operations, payer data ecosystems, cloud architecture, analytics platforms, and responsible AI practices. This person will lead solution design across areas such as member engagement, care management, utilization management, claims operations, risk adjustment, Stars, quality, provider network optimization, and administrative automation.
Location: Remote role to be located in the US
If you are a proven AI leader with deep healthcare Payer expertise—and a passion for transforming healthcare through technology—this is an opportunity to shape the future of our business. Work with our Payer AI leader, business leaders and clients discussing current state while looking at future state and bring forth technology solution ideas using AI capabilities.
What You’ll Do
- Design end\-to\-end AI and generative AI solutions for health plan use cases, including architecture, data flows, integration patterns, model selection, governance, security, and deployment approach.
- Partner with health plan stakeholders to identify high\-value AI opportunities, define business outcomes, assess feasibility, and shape implementation roadmaps.
- Lead solutioning for payer\-specific domains such as claims, enrollment, benefits, prior authorization, medical management, provider data, quality measures, risk adjustment, member services, and regulatory reporting.
- Develop reference architectures using cloud platforms, data lakes, warehouses, MLOps pipelines, APIs, workflow tools, and enterprise integration patterns.
- Guide teams on applying large language models, retrieval\-augmented generation, predictive analytics, natural language processing, intelligent automation, and decision support in healthcare settings.
- Collaborate with data scientists, engineers, clinicians, compliance teams, security architects, and delivery leaders to move concepts from prototype to production.
- Define responsible AI controls, including model monitoring, explainability, human\-in\-the\-loop review, bias assessment, auditability, privacy, and regulatory compliance.
- Support pre\-sales and client advisory activities, including solution presentations, proposals, estimates, demos, workshops, and executive briefings.
- Evaluate vendor platforms and AI tools across cloud, payer technology, data management, workflow automation, and clinical operations ecosystems.
- Create reusable assets, including solution blueprints, architecture patterns, implementation playbooks, data requirements, and governance frameworks.
- Strengthen alliances with key partners (Epic, Oracle Health, Microsoft, AWS, ServiceNow, cybersecurity vendors, Google, Leading AI players).
- Build and mentor a high\-performing team of AI leaders.
- Develop sales collateral for all solutions and services catering to healthcare payers.
What We’re Looking For
- Bachelor’s degree in computer science, information systems, engineering, data science, healthcare informatics, or a related field.
- 8\+ years of experience in technology architecture, data architecture, analytics, AI/ML, or enterprise solution delivery.
- 4\+ years of experience supporting health plans, payers, managed care organizations, or healthcare administrative platforms.
- Hands\-on experience designing AI, machine learning, generative AI, analytics, or automation solutions in production environments.
- Strong understanding of health plan business processes, including claims, membership, provider, utilization management, care management, quality, risk adjustment, and customer service.
- Experience with healthcare data standards and regulations, such as HIPAA, FHIR, HL7, X12 EDI, CMS interoperability requirements, and payer data exchange.
- Experience with major cloud platforms such as AWS, Azure, or Google Cloud, including data, AI, security, and integration services.
- Familiarity with modern AI patterns such as RAG, vector databases, prompt engineering, model evaluation, MLOps, LLMOps, and AI governance.
- Ability to communicate complex technical concepts to executive, clinical, operational, and technical audiences.
- Strong consulting, facilitation, problem\-solving, and stakeholder management skills.
Required Qualifications:
- 8\+ years of experience in designing and developing AI/ML solutions
- 4\+ years of experience in healthcare Payer technology, consulting, or systems integration.
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 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.
Where required by law, NTT DATA provides a reasonable range of compensation for specific roles. The starting pay range for this remote role is $119,578 \- $221,625\. 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 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.
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 $119K-$221K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($170K) sits 8% below the category median. Disclosed range: $119K to $221K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
NTT DATA AI Hiring
NTT DATA has 10 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Positions span Plano, TX, US, Dallas, TX, US, Atlanta, GA, US. Compensation range: $221K - $359K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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