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About This Role
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.
NTT DATA's Client is currently seeking an AI Security Architect to join their team in Boston, Massachusetts (US\-MA), United States (US).
Job Description:
AI Security Architect
Level L4
Location US, Massachusetts (Boston Area) (Hybrid Work from Client Office)
Position Start Date: 16 Aug 2026
Duration: 1 Year
Position Summary
- The AI Architect is responsible for defining the enterprise AI strategy, designing scalable AI and Generative AI solutions, and leading the technical architecture for AI\-driven products and business transformation initiatives.
- The role bridges business objectives with emerging AI technologies, ensuring secure, scalable, ethical, and compliant AI implementations across cloud and on\-premises environments.
The AI Architect works closely with business stakeholders, enterprise architects, data engineers, security teams, application developers, and data scientists to deliver production\-ready AI solutions while establishing enterprise AI governance, standards, and best practices.
Key Responsibilities
- AI Strategy \& Architecture
- Define enterprise AI architecture aligned with business strategy and digital transformation objectives.
- Design scalable AI, Machine Learning (ML), and Generative AI solution architectures.
- Develop AI reference architectures, reusable frameworks, and implementation standards.
- Evaluate emerging AI technologies and recommend adoption strategies.
- Establish enterprise AI roadmaps and technology blueprints.
- Solution Design
- Design end\-to\-end AI solutions integrating enterprise applications, cloud platforms, APIs, and data platforms.
- Architect Retrieval\-Augmented Generation (RAG), AI agents, copilots, intelligent automation, and conversational AI solutions.
- Define model selection strategies for LLMs, foundation models, and traditional ML models.
- Design vector databases, prompt engineering frameworks, embeddings, and orchestration pipelines.
AI Platform \& Engineering
- Design AI platforms leveraging Azure AI, AWS AI, Google Vertex AI, OpenAI, Anthropic, or similar technologies.
- Define scalable MLOps and LLMOps architectures.
- Establish model lifecycle management, CI/CD pipelines, monitoring, and version control.
- Optimize AI infrastructure for performance, scalability, reliability, and cost efficiency.
- Governance, Risk \& Compliance
- Establish AI governance frameworks, responsible AI principles, and model risk management practices.
- Ensure compliance with AI regulations, privacy requirements, and security standards.
- Define controls for data protection, explainability, bias detection, model monitoring, and auditability.
- Collaborate with GRC, Privacy, and Security teams to implement AI risk controls.
Security Architecture
- Design secure AI solutions following Zero Trust principles.
- Define security controls for AI models, APIs, prompts, embeddings, and training data.
- Implement identity management, encryption, access controls, and secure deployment practices.
- Address AI\-specific threats including prompt injection, model poisoning, data leakage, and adversarial attacks.
Technical Leadership
- Provide architectural guidance to AI engineers, data scientists, and development teams.
- Lead architecture reviews and technology assessments.
- Mentor technical teams on AI best practices and emerging technologies.
- Drive innovation through proof\-of\-concepts (POCs), pilots, and accelerator development.
- Stakeholder Management
- Collaborate with Enterprise Architects, CISO, business leaders to identify AI opportunities.
- Translate business requirements into AI solution architectures.
- Present architecture designs and technical recommendations to executive stakeholders.
- Support pre\-sales activities, solution proposals, and client workshops.
Required Qualifications
- Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
- Master's degree preferred.
- 10\+ years of experience in software engineering, cloud architecture, or enterprise solution architecture.
- 5\+ years of experience designing enterprise AI or Machine Learning solutions.
Required Technical Skills
- Artificial Intelligence
- Machine Learning
- Deep Learning
- Generative AI
- Large Language Models (LLMs)
- Natural Language Processing (NLP)
- Computer Vision
- Reinforcement Learning (preferred)
- GenAI Technologies
- OpenAI
- Azure OpenAI
- Anthropic Claude
- Google Gemini
- Meta Llama
- Mistral
- Hugging Face
- AI Frameworks
- LangChain
- LangGraph
- LlamaIndex
- Semantic Kernel
- CrewAI
- AutoGen
- Programming
- Python
- Java
- C\#
- REST APIs
- SQL
- JavaScript (preferred)
- Cloud Platforms
- Microsoft Azure AI
- AWS AI Services
- Google Cloud Vertex AI
- Data Technologies
- Azure Data Platform
- Databricks
- Snowflake
- Vector Databases (Pinecone, Weaviate, Milvus, Azure AI Search)
- SQL/NoSQL databases
- MLOps / LLMOps
- MLflow
- Azure ML
- Kubeflow
- Docker
- Kubernetes
- GitHub Actions
- Azure DevOps
- Security \& Governance
- AI Governance Frameworks
- Responsible AI
- Model Monitoring
- Data Privacy
- AI Risk Management
- AI Security
- India DPDP Act
- Soft Skills
- Strategic thinking and innovation
- Strong analytical and problem\-solving abilities
- Executive\-level communication and presentation skills
- Leadership and mentoring capabilities
- Stakeholder management
- Cross\-functional collaboration
- Ability to simplify complex technical concepts for business audiences
- Preferred Certifications
- Microsoft Certified: Azure AI Engineer Associate
- Microsoft Certified: Azure Solutions Architect Expert
- AWS Certified Machine Learning Engineer
- Google Professional Machine Learning Engineer
- Databricks Certified Machine Learning Professional
- NVIDIA AI Certifications
- TOGAF
- Certified Information Systems Security Professional (CISSP) (preferred)
- ISO/IEC 42001 Lead Implementer or Lead Auditor (preferred)
- Key Deliverables
- Enterprise AI Strategy and Roadmap
- AI Reference Architecture
- AI Solution Designs
- GenAI and Agentic AI Architecture
- AI Governance Framework
- AI Security Architecture
- MLOps/LLMOps Framework
- Architecture Review Reports
- Technology Evaluation and Recommendation Documents
- AI Standards and Best Practices
- Proof of Concepts (POCs) and Technical Accelerators
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 $3\.6 billion each year in R\&D to help organizations and society move confidently and sustainably into the digital future. Visit us at us.nttdata.com
NTT DATA provides a reasonable range of compensation for U.S.\-based positions. The starting pay range for this role is 110$/hr \-125$/hr. Actual compensation will depend on a number of factors, including the candidate's relevant experience, technical skills, and other qualifications.
This position is eligible for company benefits including participation in medical, dental, and vision insurance, flexible spending or health savings account, and AD\&D insurance, employee assistance, participation in a 401k program, 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 $228K-$260K range is above the 75th percentile 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($244K) sits 14% above the category median. Disclosed range: $228K to $260K.
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
AI roles in Boston pay a median of $210,000 across 166 tracked positions.
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.
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