New opportunity

AI Technical Architect

Capgemini · Hyderabad, India

About this role

Role Purpose: As an AI Technical Architect at Capgemini, you will be responsible for architecting enterprise-scale AI and agentic solutions to drive digital transformation and business innovation for global organizations. You will provide architectural leadership throughout the entire lifecycle, from discovery to implementation and enterprise-scale adoption. Responsibilities: Design end-to-end AI and agentic architectures that align with enterprise strategy and governance. Partner with business, product, and engineering teams to shape scalable solutions. Guide the adoption of generative AI, orchestration frameworks, and human-in-the-loop capabilities. Ensure that all solution designs adhere to security, compliance, resilience, and responsible AI principles. Qualifications: Requires 12+ years of professional experience. Proven track record in designing complex enterprise solutions as a Solution Architect. Strong expertise in application, data, integration, and cloud-native architecture patterns. Experience supporting AI, machine learning, analytics, automation, or digital transformation initiatives. Knowledge of generative AI, LLM platforms, agentic workflows, APIs, and orchestration frameworks. Excellent stakeholder management, communication, and architecture governance capabilities. Benefits: Opportunity to work on innovative AI and emerging technology initiatives. Access to continuous learning, certifications, and career development opportunities. Collaboration with diverse global teams across business, engineering, and technology disciplines.

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

Skills for this role

Enterprise ArchitectureGenerative AILLMAgentic WorkflowsOrchestration FrameworksCloud-native ArchitectureMachine LearningAnalyticsAutomationSolution ArchitectureStakeholder ManagementArchitecture Governance

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 AI and Agentic Architecture
The core mandate is designing end-to-end solutions that integrate generative AI and agentic workflows into existing enterprise ecosystems. You must demonstrate the ability to scale these architectures beyond prototypes, ensuring they align with complex business strategies and long-term digital transformation goals.
Governance, Security, and Responsible AI
As an architect, you are responsible for embedding security, compliance, and resilience into every design. You must be prepared to articulate how your solutions adhere to responsible AI principles, particularly when implementing human-in-the-loop capabilities in high-stakes enterprise environments.
Cross-Functional Stakeholder Leadership
Success requires bridging the gap between business, product, and engineering teams. You must be able to translate complex technical requirements into actionable architectural roadmaps while maintaining governance standards across diverse global teams.

Likely challenges to think through

Balancing Innovation with Enterprise Constraints
A significant challenge is introducing cutting-edge agentic workflows into rigid, legacy-heavy enterprise environments. Prepare by developing strategies to justify the adoption of new orchestration frameworks while addressing concerns regarding system stability, data privacy, and existing infrastructure compatibility.
Managing Ambiguity in Emerging Tech Adoption
Generative AI and agentic frameworks are rapidly evolving, making it difficult to define long-term architectural stability. You will likely face pressure to deliver solutions that are future-proof; prepare by focusing on modular, cloud-native design patterns that allow for flexibility as underlying LLM platforms and orchestration tools change.

Practice your answers

Technical practice

Designing Agentic Workflows at Scale

What to coverExplain how you architect agentic systems to ensure reliability and scalability in an enterprise environment. Discuss your approach to managing state, error handling, and the integration of human-in-the-loop checkpoints within complex workflows.

Generative AI Governance and Compliance

What to coverDescribe how you incorporate responsible AI principles and security compliance into the design phase of an LLM-based solution. Focus on how you address data privacy, model bias, and the auditability of AI-generated outputs.

Cloud-Native Architecture for AI

What to coverDiscuss the trade-offs between different cloud-native patterns when deploying large-scale machine learning models. Address how you manage infrastructure costs, latency, and model serving requirements in a multi-tenant enterprise architecture.

Orchestration Frameworks Selection

What to coverCompare different orchestration frameworks for managing LLM pipelines and agentic interactions. Explain the criteria you use to select a framework based on project requirements such as extensibility, community support, and integration with existing enterprise APIs.

Integrating AI with Legacy Systems

What to coverDetail your strategy for integrating modern AI and machine learning components into existing enterprise data and application landscapes. Focus on how you maintain data integrity and ensure seamless communication between legacy systems and new AI services.

Ensuring Resilience in AI Solutions

What to coverExplain how you design for failure in AI-driven systems, particularly when dealing with non-deterministic model outputs. Suggest strategies for fallback mechanisms and monitoring that ensure business continuity.

Data Strategy for Enterprise AI

What to coverDescribe the architectural requirements for data pipelines that support high-quality LLM performance. Address how you handle data ingestion, vectorization, and retrieval-augmented generation (RAG) at an enterprise scale.

Architecture Governance in Agile Environments

What to coverExplain how you maintain architectural standards and governance while working with cross-functional teams in an agile delivery model. Suggest ways to balance the need for speed with the necessity of long-term architectural integrity.

Behavioural practice

Managing Stakeholder Expectations

What to coverDescribe a situation where you had to align business stakeholders with technical constraints regarding an AI project. Explain how you communicated the risks and trade-offs to reach a consensus.

Leading Through Technical Ambiguity

What to coverProvide an example of a complex project where the requirements were initially unclear. Detail the steps you took to define the architectural vision and guide the engineering team toward a successful implementation.

Driving Enterprise-Scale Adoption

What to coverShare an experience where you led the adoption of a new technology or architectural pattern across a large organization. Discuss how you overcame resistance and ensured the solution was successfully integrated into business operations.

Collaborating Across Global Teams

What to coverDescribe a time you worked with a diverse, global team to deliver a technical solution. Explain how you managed communication challenges and leveraged different perspectives to improve the final architecture.

Sharpen your knowledge

Topics to prepare

Enterprise AI Governance and Responsible AI
Review frameworks for ensuring security, compliance, and resilience in AI deployments. Exercise: Draft a governance checklist for an enterprise-scale LLM implementation that addresses data privacy, bias mitigation, and human-in-the-loop oversight to align with the role's requirement for responsible AI principles.
Agentic Workflow Architecture
Study the conceptual design of autonomous agent systems, including task decomposition, memory management, and tool-use patterns. Exercise: Map out a high-level architecture for an agentic system that automates a multi-step business process, focusing on how the agent interacts with existing enterprise APIs and maintains state.
Cloud-Native Integration Patterns
Focus on how AI solutions integrate with existing enterprise data and application ecosystems. Exercise: Design a conceptual diagram showing how a generative AI service integrates with a cloud-native data lake and legacy enterprise applications, ensuring scalability and low-latency communication.

Skills to revise

Orchestration Framework Implementation
Gain hands-on familiarity with tools used to manage LLM workflows and agent chains. Exercise: Build a prototype workflow using a common orchestration framework to demonstrate how to chain multiple prompts and external tool calls to solve a specific business problem.
Stakeholder Management and Architectural Leadership
Prepare to communicate complex technical designs to non-technical business leaders. Exercise: Create a presentation outline that explains the business value, risks, and implementation roadmap of an AI transformation initiative to a C-suite audience, emphasizing the 'discovery to adoption' lifecycle.

Your preparation checklist

Prepare an enterprise AI architecture case study
Draft a technical walkthrough of a past project where you designed an end-to-end AI or machine learning solution. Focus on how you integrated cloud-native patterns, ensured data security, and managed governance at an enterprise scale.
Develop a framework for agentic workflow design
Create a high-level architectural diagram or document explaining your approach to building agentic workflows. Include how you handle orchestration frameworks, human-in-the-loop requirements, and LLM platform selection.
Map your experience to responsible AI principles
Prepare specific examples of how you have implemented compliance, security, and responsible AI guardrails in previous roles. Be ready to discuss how you balance innovation with enterprise-grade resilience.
Formulate strategic questions for the interview
Prepare questions regarding Capgemini's current AI transformation initiatives, such as how they approach the balance between custom LLM development and off-the-shelf orchestration frameworks for global clients.
Refine your stakeholder management narrative
Prepare to discuss your experience in bridging the gap between technical engineering teams and business stakeholders. Focus on how you communicate complex architectural trade-offs to non-technical leadership.

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