New opportunity

Chief Architect - Gen AI

Cognizant · Chennai, India

About this role

Cognizant seeks a Chief Architect - Gen AI to define and deliver enterprise-scale generative and agentic AI architectures for digital transformation and business innovation. The role sits within the AI & Digital Engineering team and works with C-suite stakeholders, AI strategists, solution architects and engineering teams to build scalable, secure AI ecosystems.

Responsibilities

Own the enterprise AI architecture strategy and align it with business objectives and transformation initiatives.
Design and implement generative and agentic AI solutions, including multi-agent architectures and orchestration frameworks. Lead end-to-end solution architecture from LLM selection and context engineering through deployment and integration.
Develop proofs of concept, pilots and reusable accelerators that deliver measurable business outcomes. Establish AI governance, compliance and ethical AI frameworks.

Required qualifications

18+ years of IT experience, including 5+ years in AI/ML, with experience in generative or agentic AI systems.
Proven enterprise architecture, AI strategy and digital transformation expertise; hands-on generative AI, agentic AI and multi-agent system design experience.
Deep understanding of enterprise architecture frameworks such as TOGAF and Zachman, and experience with cloud-based AI platforms including AWS, Azure and Google Cloud.

Preferred qualifications

Experience designing AI governance models, ethical AI frameworks and regulatory compliance strategies; familiarity with enterprise AI platforms from Microsoft, Google, Salesforce, ServiceNow, SAP, Oracle or Workday.
Ability to mentor architects and scale AI capabilities; experience defining agent lifecycle, observability and interoperability standards; strong executive communication and C-suite engagement.

Working arrangement: Although the page labels the work model as Hybrid, the role details specify an in-office position requiring five days a week at a client or Cognizant office in Chennai, Bangalore or Hyderabad. The listed job location is Chennai, India.

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

Skills for this role

Enterprise architectureAI strategyGenerative AIAgentic AIMulti-agent systemsAgent orchestrationSolution architectureLarge language modelsContext engineeringAI deploymentSystems integrationProof of concept developmentAI governanceRegulatory complianceEthical AITOGAFZachmanAWSAzureGoogle CloudDigital transformationAgent lifecycle managementObservabilityInteroperabilityExecutive communicationMentoring

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

Own the enterprise AI architecture strategy
The role owns a strategy aligned with business objectives and digital transformation. Prepare to explain how you would turn executive goals into architecture decisions and a delivery roadmap.
Lead generative and agentic solution design
The role covers multi-agent architectures and orchestration, from LLM selection and context engineering through deployment and integration. Prepare to walk through an end-to-end design and its key decisions.
Demonstrate measurable value
The role calls for PoCs, pilots and reusable accelerators with measurable business outcomes. Prepare to explain how you would choose success measures and decide whether a pilot is ready to scale.
Build responsible AI into the architecture
The role establishes and enforces AI governance, compliance and ethical AI frameworks. Prepare to describe where controls belong in solution design and ongoing operation.
Scale architecture across stakeholders and teams
The role works with C-suite stakeholders and engineering teams; mentoring architects and defining agent lifecycle, observability and interoperability standards are listed as differentiators. Prepare to show how you would communicate decisions and establish reusable standards.

Likely challenges to think through

Balancing agent autonomy with control
Likely preparation challenge, not a stated employer problem: greater agent autonomy may increase flexibility while making behavior harder to oversee. Prepare to explain when you would use multi-agent orchestration and how you would make its operation observable and governed.
Moving from pilot to enterprise deployment
Likely preparation challenge, not a stated employer problem: a promising PoC may still face integration, security and scalability trade-offs. Prepare a decision framework that connects pilot results to measurable outcomes and production architecture.
Reconciling executive goals with technical constraints
Likely preparation challenge, not a stated employer problem: transformation ambitions may compete with platform, compliance and delivery constraints. Prepare to explain trade-offs at both C-suite and engineering levels without treating one architecture choice as universally best.

Practice your answers

Technical practice

How would you define an enterprise AI architecture strategy aligned with a digital transformation objective?

What to coverThe role calls for ownership of enterprise AI architecture strategy and knowledge of frameworks such as TOGAF and Zachman. Suggested preparation: explain how you would translate business objectives into architecture decisions, standards and a delivery roadmap.

How would you select an LLM for an enterprise generative AI solution?

What to coverThe posting places LLM selection within end-to-end solution architecture. Suggested preparation: compare candidates against the use case, solution quality, security, integration needs and operational trade-offs.

How would you design and evaluate context engineering for a generative AI application?

What to coverContext engineering is an explicit part of the role. Suggested preparation: describe how you would choose relevant context, control access to it and test whether the resulting outputs serve the intended business task.

When would you use a multi-agent architecture, and how would you orchestrate it?

What to coverThe role includes hands-on multi-agent system design and orchestration frameworks. Suggested preparation: explain agent responsibilities, coordination and failure handling, including when a simpler design would be preferable.

How would you take a generative AI solution from design through cloud deployment and enterprise integration?

What to coverThe posting calls for end-to-end deployment and integration, with experience on cloud-based AI platforms including AWS, Azure and Google Cloud. Suggested preparation: walk through architecture boundaries, integration dependencies and the trade-offs that would guide platform selection.

How would you design a scalable, secure enterprise AI ecosystem?

What to coverBuilding scalable, secure AI ecosystems is part of the advertised work. Suggested preparation: discuss how you would identify sensitive data and system boundaries, set access controls and validate that the architecture can support broader adoption.

How would you evaluate a proof of concept before advancing it to a pilot or reusable accelerator?

What to coverThe role calls for PoCs, pilots and reusable accelerators tied to measurable business outcomes. Suggested preparation: define success measures, explain what evidence would justify progression and identify what must change before a prototype is reusable.

How would you establish AI governance, compliance and ethical AI controls?

What to coverEstablishing and enforcing these frameworks is an explicit responsibility. Suggested preparation: describe how you would assign decision ownership, review risks and test whether controls remain effective as solutions evolve, without assuming a particular regulation applies.

How would you manage and troubleshoot agents after deployment?

What to coverThe posting highlights agent lifecycle, observability and interoperability standards as differentiators. Suggested preparation: explain what you would monitor, how you would diagnose a failed or unexpected interaction and how findings would feed back into lifecycle controls.

How would you assess interoperability with existing enterprise AI platforms?

What to coverThe posting values familiarity with platforms from Microsoft, Google, Salesforce, ServiceNow, SAP, Oracle and Workday. Suggested preparation: describe how you would evaluate integration boundaries, data exchange and portability without assuming any particular platform is used in this role.

Behavioural practice

Tell me about a time you aligned an architecture decision with executive business priorities.

What to coverThe role involves AI strategy and C-suite engagement. Suggested preparation: give the situation, your actions to explain trade-offs and secure alignment, and the resulting decision or outcome.

Describe a time you coordinated strategists, architects and engineers on a complex initiative.

What to coverThe posting names collaboration across these groups as part of the role. Suggested preparation: set out the situation, how you resolved differing priorities or design views, and what the team delivered.

Tell me about a time you balanced AI innovation with governance or ethical concerns.

What to coverThe role combines innovation with responsible adoption. Suggested preparation: describe the situation, the controls or changes you advocated, and the effect on the initiative.

Describe a time you mentored architects or helped teams adopt a shared architecture approach.

What to coverMentoring architects and scaling AI capabilities are listed as differentiators. Suggested preparation: explain the situation, the specific guidance you provided and the observable result.

Tell me about a pilot or proof of concept whose results changed your proposed direction.

What to coverThe role links PoCs and pilots to measurable business outcomes. Suggested preparation: describe the initial objective, what the evidence revealed, your response and the resulting decision.

Sharpen your knowledge

Topics to prepare

Enterprise AI architecture strategy
Posting: Own AI architecture strategy aligned with business objectives and digital transformation; understand frameworks such as TOGAF and Zachman. Suggested revision exercise: Outline how each framework would help document business goals, current capabilities, target architecture and transition decisions for a hypothetical enterprise AI initiative.
Generative and agentic architecture
Posting: Design generative and agentic AI solutions, including multi-agent architectures and orchestration frameworks; agent lifecycle and interoperability standards are listed as differentiators. Suggested revision exercise: Compare a single-agent and multi-agent design for the same hypothetical workflow, explaining coordination, handoffs and lifecycle trade-offs.
LLM selection and context engineering
Posting: Lead architecture from LLM selection and context engineering through deployment and integration. Suggested revision exercise: Define selection criteria for a hypothetical enterprise use case, then explain how context design affects usefulness, security and integration.
Governance and responsible adoption
Posting: Establish AI governance, compliance and ethical AI frameworks; experience with regulatory compliance strategies is a differentiator. Suggested revision exercise: Draft a decision checklist covering ownership, review, acceptable use and compliance considerations for a hypothetical agentic solution; distinguish proposed controls from any actual company policy.
Cloud and enterprise AI ecosystems
Posting: Experience with cloud-based AI platforms including AWS, Azure and Google Cloud is required; familiarity with named enterprise AI platforms is a differentiator. Suggested revision exercise: Compare the architectural questions you would ask before placing the same AI solution on each cloud, including security, deployment and existing-system integration, without assuming a prescribed platform.

Skills to revise

Draw an end-to-end solution architecture
Posting: Lead architecture from LLM selection and context engineering to deployment and integration, building scalable, secure ecosystems. Suggested practice: Draw a hypothetical solution diagram and annotate component boundaries, data flows, security decisions and integration points; explain the principal trade-offs.
Prototype agent orchestration
Posting: Strong hands-on generative AI, agentic AI and multi-agent design experience is required. Suggested practice: Build a small illustrative multi-agent workflow, demonstrate how agents pass work between them, and identify where orchestration could fail. No particular framework is specified.
Structure a measurable pilot
Posting: Drive PoCs, pilots and reusable accelerators with measurable business outcomes. Suggested practice: Write a one-page hypothetical pilot plan with a business objective, baseline, success measure, evaluation steps and a decision on what could be reused.
Specify operating and governance checks
Posting: Establish governance, compliance and ethical AI frameworks; agent observability and lifecycle standards are differentiators. Suggested practice: Add a review checklist and an operational monitoring plan to the prototype, stating what would be checked before release and what would trigger intervention afterward.
Present an architecture decision to executives
Posting: The role works with C-suite stakeholders, and executive communication and mentoring architects are differentiators. Suggested practice: Deliver a brief, business-outcome-focused explanation of your hypothetical design, then prepare a separate technical review for architects covering trade-offs and open decisions.

Your preparation checklist

Map your experience to the stated requirements
Prepare a one-page evidence map covering the posting’s 18+ years in IT, 5+ years in AI/ML, generative or agentic AI work, enterprise architecture, and experience with AWS, Azure and Google Cloud. Use specific examples without assuming every example must come from one project.
Build an enterprise architecture walkthrough
Choose a project you can discuss and prepare a diagram showing how its architecture served business objectives, integrated with existing systems and addressed scale and security. Be ready to explain how you used, or would apply, TOGAF or Zachman.
Rehearse an end-to-end agentic AI design
As a practice exercise, sketch a multi-agent solution and explain LLM selection, context engineering, orchestration, deployment and integration trade-offs. The posting calls for these capabilities; it does not specify an interview design test.
Document a pilot and its outcomes
Prepare a concise PoC or pilot case study covering the problem, decisions, measurable results and any reusable accelerator. Distinguish observed outcomes from targets or estimates.
Prepare a responsible-AI review
Draft a short review of how you would address AI governance, compliance and ethical use, including proposed controls for agent lifecycle, observability and interoperability. Label proposed controls as your approach, not Cognizant policy.
Practice an executive-level briefing
Prepare a brief explanation of an AI architecture recommendation, its business value and key trade-offs for a C-suite audience. Have a separate example ready of mentoring architects or scaling capabilities across teams, if applicable.
Bring targeted questions about scope and location
Ask which business outcomes and enterprise platforms matter most, how architecture ownership is shared with strategists and engineering teams, and which office the role requires. The posting labels the work model Hybrid but specifies five in-office days at a client or Cognizant office in Chennai, Bangalore or Hyderabad.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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