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About This Role
Req ID: 375004
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 Solutions AI Architect, Healthcare Provider in North America to join our team in Plano, Texas (US\-TX), United States (US).
AI Solution Architect for Healthcare Provider 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 healthcare systems, hospitals, academic medical centers, physician groups, and integrated delivery networks.
This role will partner with clinical, operational, financial, digital, data, and technology leaders to translate provider business challenges into scalable AI\-enabled solutions. The ideal candidate has strong knowledge of provider operations, electronic health record ecosystems, clinical workflows, healthcare data standards, cloud architecture, analytics platforms, and responsible AI practices.
The AI Solutions Architect will lead solution design across areas such as clinical documentation, revenue cycle, patient access, capacity management, care coordination, population health, quality improvement, provider experience, patient engagement, supply chain, and administrative automation.
Location: Remote in the United States
What You’ll Do
- Design end\-to\-end AI and generative AI solutions for provider use cases, including architecture, data flows, integration patterns, model selection, workflow integration, governance, security, and deployment approach.
- Partner with healthcare system stakeholders to identify high\-value AI opportunities, define business outcomes, assess feasibility, and shape implementation roadmaps.
- Lead solutioning for provider\-specific domains such as electronic health records, clinical documentation, patient access, scheduling, revenue cycle, coding, billing, denials management, care coordination, population health, quality reporting, command centers, and patient engagement.
- Develop reference architectures using cloud platforms, EHR integration services, data lakes, warehouses, lakehouses, MLOps pipelines, APIs, interoperability layers, workflow tools, and enterprise integration patterns.
- Guide teams on applying large language models, retrieval\-augmented generation, predictive analytics, natural language processing, computer vision, intelligent automation, and decision support in healthcare delivery settings.
- Collaborate with data scientists, engineers, clinicians, informaticists, compliance teams, security architects, revenue cycle leaders, and delivery teams to move concepts from prototype to production.
- Define responsible AI controls, including model monitoring, explainability, clinical safety review, human\-in\-the\-loop validation, bias assessment, auditability, privacy, cybersecurity, and regulatory compliance.
- Support pre\-sales and client advisory activities, including solution presentations, proposals, estimates, demos, workshops, executive briefings, and innovation strategy sessions.
- Evaluate vendor platforms and AI tools across EHR ecosystems, cloud, clinical documentation, revenue cycle, patient engagement, workflow automation, and analytics.
- Create reusable assets, including provider AI solution blueprints, architecture patterns, implementation playbooks, data requirements, governance frameworks, and value case templates.
What We’re Looking For
- Bachelor’s degree in computer science, information systems, engineering, data science, healthcare informatics, clinical informatics, or a related field.
- 8\+ years of experience in technology architecture, data architecture, analytics, AI/ML, enterprise solution delivery, or healthcare technology consulting.
- 4\+ years of experience supporting healthcare providers, health systems, hospitals, academic medical centers, physician groups, or integrated delivery networks.
- Hands\-on experience designing AI, machine learning, generative AI, analytics, or automation solutions in production environments.
- Strong understanding of provider business and clinical processes, including patient access, EHR workflows, clinical documentation, care delivery, revenue cycle, quality reporting, population health, and patient engagement.
- Experience with healthcare data standards and regulations, such as HIPAA, FHIR, HL7, CDA, X12, USCDI, interoperability requirements, and clinical data exchange.
- Experience with major cloud platforms such as AWS, Azure, or Google Cloud, including data, AI, security, integration, and governance services.
- Familiarity with modern AI patterns such as RAG, vector databases, prompt engineering, model evaluation, MLOps, LLMOps, clinical validation, and AI governance.
- Ability to communicate complex technical concepts to executive, clinical, operational, financial, and technical audiences.
- Strong consulting, facilitation, problem\-solving, stakeholder management, and executive presentation skills.
Required Qualifications:
- 8\+ years of experience in designing and developing AI/ML solutions
- 4\+ years of experience in healthcare Provider 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.
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.
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.
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 above the median for AI Architect roles in our dataset (median: $172K across 30 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 4,133 AI roles we're tracking, AI Architect positions make up 1% of the market. At NTT DATA, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $215,000 based on 115 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($170K) sits 21% 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 Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
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).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
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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