New opportunity

Systems Integration Specialist Advisor

NTT Data · Noida, India

About this role

The Systems Integration Specialist Advisor (Senior AI Architect) will lead the design, implementation, and governance of enterprise-scale AI platforms integrated with DevOps, Cloud, and Security frameworks. This role requires a strategic technologist capable of architecting production-grade AI/ML and GenAI solutions while embedding secure DevSecOps practices across multi-cloud environments. Key responsibilities include defining scalable AI reference architectures, leading the end-to-end AI lifecycle, establishing standards for AI explainability and ethics, and architecting secure AI systems adhering to Zero Trust principles. The role also involves driving innovation, providing technical mentorship, and collaborating with stakeholders to define AI transformation roadmaps. Required qualifications include 7-12+ years of experience in enterprise architecture, cloud engineering, or platform leadership, with at least 5 years specifically designing and deploying AI/ML or GenAI solutions in production. Candidates must possess strong expertise in Python, AI frameworks (TensorFlow, PyTorch, LangChain), LLM architecture, RAG systems, and agentic frameworks, alongside hands-on experience with Kubernetes, Docker, CI/CD pipelines, and Infrastructure as Code (Terraform, ARM, CloudFormation). Preferred qualifications include experience building enterprise AI platforms (AIOps, self-healing systems), knowledge of data governance and knowledge graphs, experience integrating AI with ITSM platforms (ServiceNow, Remedy), and relevant cloud or security certifications (AWS/Azure Architect, CISSP, CCSP).

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

AI ArchitectureMachine LearningGenerative AILLMRAGAgentic AIAIOpsPythonTensorFlowPyTorchLangChainAWSAzureGCPKubernetesDockerCI/CDTerraformARMCloudFormationDevSecOpsZero TrustDistributed Systems

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Enterprise AI Platform Architecture
The core requirement is to design and govern production-grade AI/ML and GenAI platforms. You must demonstrate the ability to build scalable reference architectures that integrate LLMs, RAG systems, and agentic frameworks into existing enterprise environments.
Secure DevSecOps Integration
You are expected to embed security directly into the AI lifecycle. This involves architecting systems under Zero Trust principles, performing threat modeling for AI-specific risks like prompt injection or model poisoning, and ensuring compliance through automated CI/CD pipelines.
Strategic AI Transformation Leadership
Beyond technical execution, you must act as a strategic advisor to stakeholders. This requires translating complex AI capabilities into business value, defining transformation roadmaps, and providing technical mentorship to engineering teams.

Likely challenges to think through

Balancing Innovation with Governance
A significant challenge is maintaining the speed of AI innovation while enforcing strict standards for explainability, ethics, and data governance. Prepare by discussing how you have implemented guardrails that allow for experimentation without compromising enterprise security or regulatory compliance.
Multi-Cloud and Hybrid Integration
Architecting AI solutions across diverse cloud environments (AWS, Azure, GCP) while integrating with legacy ITSM platforms like ServiceNow or Remedy is complex. Prepare by highlighting your experience in building resilient, high-availability distributed systems that maintain performance across heterogeneous infrastructure.

Practice your answers

Technical practice

Designing Scalable RAG Architectures

What to coverExplain how you would architect a Retrieval-Augmented Generation system for enterprise use. Focus on data ingestion pipelines, vector database selection, retrieval strategies, and how you ensure the system remains performant and accurate at scale.

Implementing Zero Trust in AI Workloads

What to coverDescribe how you apply Zero Trust principles to AI/ML platforms. Discuss strategies for securing model endpoints, managing secrets, and ensuring data privacy throughout the model lifecycle.

MLOps and CI/CD for AI

What to coverDetail your approach to building CI/CD pipelines specifically for AI/ML models. Address how you handle automated testing, model versioning, and deployment strategies like canary or blue-green deployments in a Kubernetes environment.

Mitigating AI Security Risks

What to coverHow do you conduct threat modeling for AI systems? Discuss specific techniques to prevent prompt injection, model poisoning, and data leakage, and how you monitor for model drift in production.

Agentic Frameworks and Orchestration

What to coverDiscuss your experience with agentic frameworks. Explain the challenges of orchestrating multiple AI agents in a production environment and how you manage state, communication, and error handling between agents.

Infrastructure as Code for AI Platforms

What to coverExplain how you use tools like Terraform or CloudFormation to manage AI infrastructure. Focus on how you maintain consistency across multi-cloud environments and ensure that infrastructure deployments are reproducible and secure.

AI Explainability and Governance

What to coverWhat frameworks or methodologies do you use to ensure AI explainability and ethical compliance? Suggestion: Prepare to discuss how you balance model performance with the need for transparency and auditability in enterprise settings.

AIOps and Self-Healing Systems

What to coverDescribe your experience building AIOps solutions. Focus on how you integrate AI to monitor and automate the remediation of infrastructure issues, and how you measure the success of these self-healing systems.

Behavioural practice

Translating AI Strategy to Business Value

What to coverDescribe a time you had to explain a complex AI architectural decision to non-technical stakeholders. How did you align the technical roadmap with business objectives and secure buy-in?

Technical Mentorship and Leadership

What to coverGive an example of how you have mentored engineering teams on AI best practices. How do you foster a culture of innovation while maintaining rigorous governance standards?

Managing Cross-Functional AI Projects

What to coverDescribe a challenging situation where you had to collaborate with security, DevOps, and data teams to deliver an AI project. How did you resolve conflicting priorities or technical disagreements?

Navigating Technical Trade-offs

What to coverTell us about a time you had to choose between two competing AI technologies or architectural approaches. What criteria did you use to evaluate them, and what was the long-term impact of your decision?

Sharpen your knowledge

Topics to prepare

Enterprise AI Governance and Ethics
Review frameworks for AI explainability, model monitoring, and risk assessment. Exercise: Draft a governance policy for a hypothetical RAG system that addresses potential prompt injection and model drift. This is relevant to the role's requirement for establishing standards for AI ethics and security compliance.
Zero Trust Architecture for AI
Study the integration of Zero Trust principles within AI pipelines, focusing on data protection and access control. Exercise: Map out a security architecture for a production-grade LLM deployment that ensures data isolation and secure secrets management. This aligns with the requirement to architect secure AI systems.
AI Transformation Roadmaps
Understand how to align AI initiatives with enterprise business objectives. Exercise: Create a high-level roadmap for transitioning a legacy system to an AI-integrated platform, identifying key milestones and stakeholder touchpoints. This is essential for the strategic leadership component of the role.

Skills to revise

RAG and Agentic Frameworks
Practice building and deploying Retrieval-Augmented Generation systems using LangChain. Exercise: Build a prototype that connects a vector database to an LLM to answer domain-specific queries. This is a direct requirement for the role's focus on GenAI solutions.
MLOps and CI/CD for AI
Master the automation of model deployment pipelines using Kubernetes and Terraform. Exercise: Write a Terraform script to provision a scalable inference environment on a cloud provider and configure a CI/CD pipeline to automate model testing. This addresses the hands-on platform engineering requirements.
Distributed Systems Design
Focus on designing high-availability architectures for AI workloads. Exercise: Design a system architecture that handles high-concurrency model inference across multiple availability zones. This is relevant to the requirement for building resilient, production-grade AI platforms.

Your preparation checklist

Map your AI production experience to the lifecycle
Prepare a concise walkthrough of a production-grade AI/ML or GenAI solution you led. Focus on the end-to-end lifecycle: from initial architecture and model selection (LLMs, RAG) to deployment, monitoring, and governance. Be ready to explain how you handled model drift, explainability, and ethical considerations.
Develop a DevSecOps and Zero Trust integration strategy
Create a brief architectural diagram or summary explaining how you embed security into AI pipelines. Highlight your experience with Zero Trust principles, threat modeling for AI (e.g., prompt injection, model poisoning), and how you integrate these into CI/CD workflows using tools like Terraform or Kubernetes.
Prepare examples of scalable cloud-native AI infrastructure
Be ready to discuss your experience designing distributed systems on AWS, Azure, or GCP. Focus on how you optimized performance, ensured high availability, and utilized containerization (Docker/Kubernetes) to support enterprise-scale AI workloads.
Formulate strategic questions for the interviewers
Prepare thoughtful questions regarding NTT Data's current AI transformation roadmap, their approach to AI governance and ethics, or how they integrate AI with existing ITSM platforms like ServiceNow. This demonstrates your strategic mindset and alignment with their business goals.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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