New opportunity

AI Architect Senior Advisor

NTT Data · Bangalore, India

About this role

The AI Architect Senior Advisor at NTT DATA is responsible for architecting and delivering GenAI, LLM, and agentic AI solutions for large-scale AI transformation programs. This role involves designing enterprise-level scalable AI-ready architectures, leading proposals and estimations, and acting as a trusted advisor to clients. The position requires 10 to 13 years of total IT experience, with at least 5 years of specific expertise in AI/ML and GenAI architecture.

Responsibilities

Architect and deliver GenAI, LLM, and agentic AI solutions.
Design enterprise-level scalable AI-ready architectures.
Lead proposals, estimations, and RFP/RFI responses.
Partner with clients as a trusted AI advisor for transformation roadmaps.
Build reference architectures and reusable GenAI frameworks.
Manage stakeholder relationships and provide technical leadership.

Required qualifications

10 to 13 years of total IT experience, including 5+ years in AI/ML/GenAI architecture.
Strong foundation in data science, ML model lifecycle, and MLOps.
Proven expertise in GenAI, LLM, and agentic architecture design.
Hands-on architecture experience with at least one major Hyperscaler (Azure, GCP, or AWS).
Understanding of data engineering pipelines.
Proven track record in RFPs, proposals, and estimation for AI programs.
Excellent stakeholder management, communication, and leadership abilities.

Preferred qualifications

Certifications in Azure AI Architect, GCP ML Engineer, or AWS Machine Learning Specialty.
Experience with enterprise-scale GenAI deployments such as copilots, chatbots, and autonomous agents.
Experience with Databricks, Snowflake, or Palantir.
Exposure to regulatory-compliant AI systems in sectors like BFSI, Healthcare, Manufacturing, or Public Sector.

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

Skills for this role

Generative AILLMAgentic AIAI/ML ArchitectureData ScienceMLOpsLLMOpsData EngineeringAzureGCPAWSDatabricksSnowflakePalantirSolution ArchitectureStakeholder Management

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-Scale GenAI Architecture Design
The role requires designing scalable, production-ready AI architectures that integrate LLMs and agentic frameworks. You must demonstrate the ability to move beyond prototypes to build robust, reusable reference architectures that support large-scale transformation programs.
Pre-Sales Leadership and Estimation
A core priority is leading the commercial side of AI delivery, specifically managing RFPs, RFI responses, and technical estimations. You must be able to translate complex technical requirements into accurate project scopes and business proposals for diverse clients.
Trusted Client Advisory
You will act as a primary technical advisor for client transformation roadmaps. This requires balancing deep technical knowledge of cloud hyperscalers (Azure, AWS, or GCP) with the ability to communicate strategic value to non-technical stakeholders.
Operationalizing AI (MLOps/LLMOps)
Success depends on integrating AI solutions into existing data engineering pipelines. You must prioritize the lifecycle management of models, ensuring that deployments are maintainable, scalable, and compliant with industry-specific regulations.

Likely challenges to think through

Balancing Innovation with Regulatory Compliance
Implementing GenAI in sectors like BFSI or Healthcare requires navigating strict data privacy and security constraints. Prepare by reviewing frameworks for responsible AI and demonstrating how you design architectures that satisfy both performance needs and regulatory mandates.
Managing Ambiguity in Client Requirements
As a senior advisor, you will often face ill-defined client problems during the proposal phase. Prepare by practicing how you scope ambiguous requirements into concrete, deliverable AI architectures while managing client expectations regarding timelines and technical feasibility.
Technical Breadth Across Hyperscalers
While you may specialize in one cloud provider, the role demands a high-level understanding of the broader ecosystem. Prepare by articulating how you adapt architectural patterns across Azure, GCP, and AWS to ensure the best fit for a client's specific infrastructure.

Practice your answers

Technical practice

Designing Scalable Agentic AI Architectures

What to coverExplain how you design agentic AI systems for enterprise scale. Discuss your approach to managing agent orchestration, state management, and the trade-offs between autonomous agent reliability and human-in-the-loop oversight.

LLMOps and Model Lifecycle Management

What to coverDescribe your strategy for implementing LLMOps within a large-scale transformation program. Focus on how you handle model versioning, evaluation metrics, drift detection, and the integration of LLMs into existing MLOps pipelines.

Hyperscaler AI Architecture Selection

What to coverCompare the AI-specific service offerings of Azure, GCP, and AWS. Explain how you determine which hyperscaler is most appropriate for a client's specific regulatory and performance requirements.

Data Engineering for GenAI Readiness

What to coverDiscuss the role of data engineering pipelines in supporting GenAI solutions. Address how you ensure data quality, security, and accessibility for RAG (Retrieval-Augmented Generation) architectures.

Building Reusable GenAI Frameworks

What to coverExplain your methodology for creating reusable reference architectures. How do you balance the need for standardized, scalable frameworks with the requirement for client-specific customization?

Estimation and Proposal Strategy for AI Programs

What to coverDescribe your process for estimating complex AI transformation projects. Detail how you account for uncertainty in model performance, data readiness, and infrastructure costs when responding to RFPs.

Regulatory Compliance in AI Systems

What to coverHow do you architect AI systems to meet strict regulatory requirements in sectors like BFSI or Healthcare? Discuss your approach to data privacy, model explainability, and auditability.

Integrating Databricks or Snowflake in AI Workflows

What to coverExplain how you leverage platforms like Databricks or Snowflake to enhance AI/ML workflows. Focus on their utility in data preparation, feature engineering, and model deployment at scale.

Behavioural practice

Acting as a Trusted Advisor

What to coverDescribe a situation where you had to guide a client through a complex AI transformation roadmap. How did you manage their expectations while ensuring the technical feasibility of the proposed solution?

Managing Stakeholder Conflict

What to coverProvide an example of a time you had to manage conflicting priorities between technical teams and business stakeholders during an AI project. What actions did you take to align them?

Leading Through Technical Ambiguity

What to coverTell me about a time you led a proposal for a large-scale AI program with significant technical uncertainty. How did you structure your approach to provide a reliable estimation and clear value proposition?

Mentoring and Technical Leadership

What to coverDescribe a time you provided technical leadership to a team working on a challenging GenAI deployment. How did you foster growth while ensuring the project met its delivery milestones?

Sharpen your knowledge

Topics to prepare

Agentic AI Architecture
Review the conceptual differences between standard LLM chains and agentic workflows, specifically focusing on autonomous decision-making, tool-use, and memory management. Exercise: Draft a high-level architecture diagram for an autonomous agent system that handles multi-step enterprise tasks, identifying where human-in-the-loop checkpoints are necessary for regulatory compliance.
Enterprise AI Proposal and Estimation
Understand the methodology for scoping large-scale AI transformation programs, including resource estimation, timeline planning, and risk assessment. Exercise: Create a mock estimation template for an enterprise GenAI deployment, breaking down costs and efforts across data preparation, model fine-tuning, infrastructure, and MLOps maintenance.
MLOps and LLMOps Lifecycle
Focus on the operationalization of AI models, including CI/CD for ML, model monitoring, and drift detection. Exercise: Map out a production-grade LLMOps pipeline that includes automated evaluation of model outputs for accuracy and safety, relevant to the requirement for scalable, enterprise-ready architectures.

Skills to revise

Hyperscaler AI Services (Azure/GCP/AWS)
Gain hands-on familiarity with native AI services on at least one major cloud provider. Exercise: Design a reference architecture using cloud-native tools (e.g., Azure AI Studio, AWS Bedrock, or GCP Vertex AI) to deploy a RAG-based chatbot, ensuring data security and scalability.
Data Engineering for AI
Practice designing data pipelines that feed into AI models, focusing on data quality, ingestion, and feature stores. Exercise: Outline a data pipeline architecture that integrates with Databricks or Snowflake to provide real-time, clean data for an LLM application.
Stakeholder Communication
Refine the ability to translate complex technical AI concepts into business value for non-technical stakeholders. Exercise: Prepare a 5-minute executive summary pitch for a GenAI transformation roadmap, focusing on ROI, risk mitigation, and strategic alignment.

Your preparation checklist

Prepare a GenAI Architecture Case Study
Develop a concise walkthrough of an enterprise-scale GenAI or agentic AI solution you have architected. Focus on the end-to-end lifecycle, including data ingestion pipelines, LLM selection, MLOps/LLMOps integration, and how you ensured scalability and regulatory compliance.
Document Your Proposal and Estimation Experience
Prepare specific examples of how you have led RFP/RFI responses for AI transformation programs. Be ready to explain your methodology for estimating project timelines, resource requirements, and technical complexity for large-scale AI engagements.
Map Your Cloud Expertise to NTT Data Requirements
Create a summary of your hands-on experience with Azure, AWS, or GCP specifically regarding AI/ML services. Highlight how you have utilized these platforms to deploy production-grade AI systems, referencing specific tools like Databricks, Snowflake, or Palantir if applicable.
Formulate Strategic Questions for the Interviewer
Prepare thoughtful questions regarding NTT Data's current approach to reusable GenAI frameworks, their process for managing AI governance in regulated sectors, and how the Senior Advisor role collaborates with clients to define long-term AI transformation roadmaps.

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