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About This Role
NTT DATA's Client is currently seeking an AI Architect to join their team in Ft. Worth, Texas (US\-TX), United States (US). (DFW area)
Seeking an experienced AI Architect to design and lead enterprise\-scale AI, ML, and Generative AI solutions built on AWS and Azure as the core AI foundation, with Microsoft Copilot as the primary user experience layer. The role is responsible for designing the end\-to\-end AI solution architecture, ensuring alignment with enterprise systems, scalability, and governance standards while integrating AI into the broader IT landscape. It requires deep expertise in RAG (Retrieval\-Augmented Generation) and Agentic AI architecture on cloud\-native platforms, enabling intelligent, scalable, and production\-ready AI systems after understanding the current product architecture. The candidate should also be able to conduct POCs to demonstrate proof of design considerations.
Platform \& Enablement Roles
AI Platform Admin (M365, copilot Studio) Manages AI platforms and environments, including access provisioning, governance controls, and policy enforcement (e.g., DLP, security, and compliance).
AI Reusable Utility Develops reusable components (e.g., prompts, connectors, APIs, templates) to accelerate AI solution delivery and promote standardization across use cases.
AI Common Infrastructure, Framework \& Observability Architect (AWS and Azure) Designs and maintains the foundational AI infrastructure, frameworks, and observability capabilities (telemetry, monitoring, metrics) required for scalable, reliable, and governed AI operations.
Core Responsibilities
Architectural Design: Define the end\-to\-end blueprints spanning data ingestion, model training, inference, and continuous monitoring. design end\-to\-end artificial intelligence solutions ensuring models scale efficiently align with enterprise systems and meet governance standards. They act as the vital bridge linking theoretical AI models built by data scientists with production\-ready, secure applications integrated into the broader IT landscape.
Enterprise Integration: Seamlessly embed AI/ML features and multi\-agent workflows into legacy applications, ERPs, and cloud\-native systems.
Governance \& Compliance: Implement ethical AI guardrails, model risk management, data privacy protections and explainability standards.
Scalability \& MLOps: Establish CI/CD for AI, model versioning, automated retraining, and drift detection to prevent performance degradation.
Tech Stack Strategy: Make crucial "build vs. buy " decisions for infrastructure, weighing tradeoffs of on\-premises, hybrid, and cloud environments.
Leadership \& Collaboration:
Serve as a technical thought leader for AI, GenAI, and data platforms.
Mentor data scientists, ML engineers, and data engineers.
Collaborate with business and product teams to translate requirements into AI\-driven solutions.
Evaluate emerging AI technologies and guide strategic adoption.
AI, ML \& GenAI Architecture
Design and define end\-to\-end AI solution architectures covering data ingestion, model training, deployment, monitoring, and governance, ensuring alignment with enterprise systems and IT landscape while meeting scalability and governance standards.
Design scalable, cloud\-native AI platforms on AWS and Azure.
Architect solutions for both batch and real\-time inference workloads.
RAG (Retrieval\-Augmented Generation)
Architect and implement RAG pipelines using structured and unstructured enterprise data.
Design ingestion, chunking, embedding, and retrieval strategies for RAG systems.
Integrate vector databases (e.g., Pinecone, FAISS, Milvus, Azure AI Search, Amazon OpenSearch).
Ensure relevance, freshness, observability, and security of RAG\-based AI systems.
Agentic AI \& Autonomous Systems
Design Agentic AI architecture enabling autonomous decision\-making and task execution.
Orchestrate multi\-agent systems using tools, memory, and reasoning workflows.
Implement guardrails, human\-in\-the\-loop controls, and observability for agent\-based systems.
Enable enterprise use cases such as AI assistants, Microsoft Copilot\-integrated workflows, task automation, and decision intelligence.
MLOps \& LLMOps
Define and implement MLOps / LLMOps frameworks for CI/CD, versioning, monitoring, and drift detection.
Enable experimentation, evaluation, and governance of ML models and LLM\-based systems.
Ensure compliance with security, privacy, and responsible AI guidelines.
Cloud \& Platform Engineering
Architect AI solutions on AWS and Azure as the primary cloud platforms, integrating Microsoft Copilot as the enterprise user experience layer.
Integrate AI platforms with enterprise applications, APIs, and data sources.
Design highly available, secure, and scalable AI systems.
Required Skills
Engineering Foundation: 7\+ years of deep knowledge of MLOps, containerization (Docker/Kubernetes), and CI/CD pipelines.
Cloud Platforms: 5\+ years of advanced expertise in deploying on major hyperscalers like AWS Machine Learning, Azure AI, or Google Vertex AI.
Data Management: 5\+ years of Proficiency in designing feature stores, vector databases, and real\-time/batch data pipelines.
AI/ML Frameworks: 3 to 5 years of familiarity with concepts like Large Language Models (LLMs), Generative AI, Retrieval\-Augmented Generation (RAG), and frameworks like PyTorch or TensorFlow.
\#LI\-NorthAmerica
About NTT DATA:
NTT DATA is a $30 billion trusted global innovator of business and technology services. We serve 75% of the Fortune Global 100 and are committed to helping clients innovate, optimize and transform for long term success. As a Global Top Employer, we have diverse experts in more than 50 countries and a robust partner ecosystem of established and start\-up companies. Our services include business and technology consulting, data and artificial intelligence, industry solutions, as well as the development, implementation and management of applications, infrastructure and connectivity. We are one of the leading providers of digital and AI infrastructure in the world. NTT DATA is a part of NTT Group, which invests over $3\.6 billion each year in R\&D to help organizations and society move confidently and sustainably into the digital future. Visit us at us.nttdata.com
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.
Where required by law, NTT DATA provides a reasonable range of compensation for specific roles. The starting hourly range for this remote role is ($80 \- 90/hourly ). This range reflects the minimum and maximum target compensation for the position across all US locations. Actual compensation will depend on several factors, including the candidate's actual work location, relevant experience, technical skills, and other qualifications.
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 AD\&D 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.
Salary Context
This $166K-$187K range is above the median for AI Architect roles in our dataset (median: $169K across 31 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 3,823 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 $212,500 based on 108 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($176K) sits 17% below the category median. Disclosed range: $166K to $187K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
NTT DATA AI Hiring
NTT DATA has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Positions span Dallas, TX, US, Atlanta, GA, US, Fort Worth, TX, US. Compensation range: $187K - $359K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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