New opportunity

Chief Architect - Gen AI

Cognizant · Chennai, India

About this role

As Chief Architect - Gen AI in Cognizant’s AI & Digital Engineering team, define and deliver enterprise-scale generative and agentic AI architectures that support digital transformation and business innovation. Collaborate 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 AI 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 with measurable business outcomes. Establish AI governance, compliance and ethical AI frameworks.

Required qualifications

18+ years of IT experience, including 5+ years in AI/ML and experience with generative AI or agentic AI systems.
Proven expertise in enterprise architecture, AI strategy and digital transformation; hands-on experience designing generative AI, agentic AI and multi-agent systems.
Deep understanding of enterprise architecture frameworks such as TOGAF and Zachman, plus 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, define agent lifecycle, observability and interoperability standards, and communicate with C-suite stakeholders.

Work arrangement: The role specifies five days a week in a client or Cognizant office in Chennai, Bangalore or Hyderabad. The page also labels its work model Hybrid, but the role-specific description calls it in-office; arrangements may change with project and client requirements.

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

Skills for this role

Generative AIAgentic AIMachine LearningEnterprise ArchitectureAI StrategyDigital TransformationMulti-Agent SystemsAI OrchestrationLarge Language ModelsLLM SelectionContext EngineeringSolution ArchitectureAI GovernanceResponsible AIRegulatory ComplianceTOGAFZachman FrameworkAWSMicrosoft AzureGoogle CloudProof of Concept DevelopmentAI ObservabilityInteroperabilityExecutive 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

Align AI architecture with transformation goals
The role owns enterprise AI architecture strategy and works with C-suite stakeholders, AI strategists and delivery teams. Preparation suggestion: be ready to explain how architectural decisions connect to business objectives and gain agreement across those groups.
Lead end-to-end generative and agentic AI design
The posting calls for hands-on multi-agent and orchestration design, from LLM selection and context engineering through deployment and integration. Preparation suggestion: organize a design walkthrough around decisions, alternatives and enterprise constraints rather than individual tools.
Turn experiments into measurable outcomes
PoCs, pilots and reusable accelerators are explicit responsibilities, with measurable business outcomes expected. Preparation suggestion: explain how to define success measures, evaluate a pilot and decide whether an approach is worth reusing.
Build governance into adoption
The architect must establish and enforce AI governance, compliance and ethical AI frameworks. Preparation suggestion: describe how controls would be incorporated into architecture and delivery decisions, not treated as a final review.
Scale architectural practice across teams
Mentoring architects and defining agent lifecycle, observability and interoperability standards are listed as differentiators. Preparation suggestion: consider how to communicate standards that teams can apply while allowing justified exceptions.

Likely challenges to think through

Agent capability versus operational complexity
Preparation hypothesis, not a stated Cognizant problem: multi-agent orchestration may offer flexibility while adding coordination and observability demands. Prepare a decision framework for when multiple agents are warranted and how their behavior would be monitored.
Pilot speed versus production readiness
Preparation hypothesis: moving from PoCs to deployed, integrated solutions may require trading rapid experimentation against security, governance and measurable value. Prepare to explain the criteria you would use before advancing a pilot.
Common standards versus platform-specific needs
Preparation hypothesis: enterprise-wide architecture across cloud AI platforms and other enterprise ecosystems may make uniform standards difficult to apply. Prepare to distinguish principles that should remain consistent from integration choices that may vary.
Executive clarity versus technical precision
Preparation hypothesis: working with both C-suite stakeholders and engineering teams may require different levels of detail without changing the underlying recommendation. Prepare a concise business rationale alongside the technical trade-offs for a major architecture decision.

Practice your answers

Technical practice

How would you define an enterprise AI architecture strategy that supports a digital transformation objective?

What to coverSuggested preparation: Explain how you would translate business objectives into architecture principles, a target state and a delivery roadmap. Discuss where TOGAF or Zachman could help structure decisions without treating either framework as a substitute for business outcomes.

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

What to coverSuggested preparation: Compare task performance, data handling, latency, cost and deployment constraints. Explain how you would test candidates against the intended use case before committing to an architecture.

How would you design context engineering for an enterprise AI application?

What to coverSuggested preparation: Describe how relevant enterprise context would be selected, kept current and supplied within model limits. Address the trade-off between answer quality, access controls, latency and cost.

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

What to coverSuggested preparation: Explain how you would assign responsibilities, coordinate handoffs and handle failed or conflicting agent outputs. Compare the added flexibility with the complexity of operating multiple agents.

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

What to coverSuggested preparation: Walk through architecture boundaries, security, scalability and integration with existing systems. Discuss how you would compare relevant capabilities of AWS, Azure and Google Cloud against the enterprise's constraints rather than assuming one platform fits every case.

How would you define agent lifecycle and observability standards?

What to coverSuggested preparation: Cover versioning, deployment controls and the signals needed to investigate agent failures or unexpected behavior. Explain how those standards would support troubleshooting across teams.

How would you build governance, compliance and ethical AI into an enterprise architecture?

What to coverSuggested preparation: Describe decision rights, review points and controls spanning design, deployment and ongoing operation. Explain how you would adapt controls to applicable obligations without assuming a particular regulation applies.

How would you decide whether an AI proof of concept should become a pilot or reusable accelerator?

What to coverSuggested preparation: Define a measurable business outcome, technical acceptance criteria and evidence needed at each stage. Address the trade-off between tailoring a solution to one use case and making components reusable.

How would you approach interoperability across agents and enterprise AI platforms?

What to coverSuggested preparation: Discuss interface contracts, data exchange and ownership boundaries that would let components evolve independently. Use the posting's emphasis on interoperability and enterprise platforms as context, without assuming a particular vendor stack.

Behavioural practice

Tell me about a time you aligned senior stakeholders on an AI architecture decision.

What to coverSuggested preparation: Use a real situation, your actions and the result. Show how you explained trade-offs and connected a technical decision to business objectives.

Tell me about a time you coordinated strategists, architects and engineers on a complex initiative.

What to coverSuggested preparation: Describe the situation, what you did to resolve differing priorities or dependencies, and the result. Make your own contribution distinct from the team's collective work.

Tell me about a time you mentored architects or helped scale a technical capability across teams.

What to coverSuggested preparation: Give the situation, the practices or guidance you introduced, and the resulting change. Distinguish evidence of adoption from an intended benefit.

Tell me about a time you addressed a governance or ethical concern that conflicted with delivery goals.

What to coverSuggested preparation: Describe the real concern, your actions with relevant stakeholders, and the outcome. Explain the trade-off without implying that speed or compliance could simply be ignored.

Tell me about a time a proof of concept or pilot did not initially show the expected value.

What to coverSuggested preparation: Set out the original objective, the evidence you gathered, what you changed or recommended, and the result. A decision not to proceed can be a useful example if you explain its basis.

Sharpen your knowledge

Topics to prepare

Enterprise AI strategy and architecture frameworks
Required: the role owns AI architecture strategy aligned to business objectives and calls for deep understanding of frameworks such as TOGAF and Zachman. Revise by mapping a hypothetical transformation objective to architecture decisions, then explain how each framework would structure the review.
Generative and multi-agent architecture
Required: the posting calls for hands-on generative AI, agentic AI, multi-agent design and orchestration. Revise by sketching agent responsibilities, coordination, boundaries and failure handling for a hypothetical enterprise workflow.
End-to-end LLM solution decisions
The role spans LLM selection and context engineering through deployment and integration. Revise by comparing two hypothetical solution designs and explaining how context, security, scalability and integration needs affect the choice.
Cloud-based enterprise AI ecosystems
Experience with AWS, Azure and Google Cloud AI platforms is listed as required. Revise by comparing architecture considerations across those platforms without assuming a particular service is mandated for this role.
Governance, ethics and agent standards
Establishing AI governance, compliance and ethical AI frameworks is a responsibility; agent lifecycle, observability and interoperability standards are listed as differentiators. Revise by outlining review checkpoints and evidence needed to operate a hypothetical agentic solution responsibly.

Skills to revise

Draw an integrated solution blueprint
Practice producing a diagram that connects LLM selection, context flow, agent orchestration, deployment and enterprise integration. Explain the major trade-offs; the posting assigns end-to-end solution architecture to this role.
Scope a measurable pilot
Draft a one-page plan for a hypothetical PoC, including its business objective, success measures, pilot boundary and criteria for a reusable accelerator. This directly reflects the responsibility for PoCs, pilots and measurable outcomes.
Apply governance to a design
Annotate the pilot blueprint with proposed compliance and ethical-AI controls, owners and review points. This is a preparation exercise for the stated governance responsibility, not a claim about Cognizant’s specific control process.
Present architecture to executives
Rehearse a short explanation of a proposed AI architecture that leads with business outcomes, then addresses risks and trade-offs for C-suite stakeholders. The posting explicitly calls for executive collaboration and communication.
Review and scale architects’ work
As preparation for the preferred mentoring capability, practice giving structured feedback on a hypothetical agent design and identifying which decisions could become shared standards across teams.

Your preparation checklist

Map evidence to the stated requirements
Create a one-page index of examples covering the posting’s 18+ years in IT, 5+ years in AI/ML, generative or agentic AI experience, enterprise architecture and digital transformation. Note where each example can be discussed without sharing confidential material.
Build an enterprise strategy walkthrough
Prepare one project account showing the business objective, architecture decisions, stakeholder trade-offs and measurable outcome. The role calls for owning AI architecture strategy and aligning it with transformation initiatives.
Practice an end-to-end agentic AI design
Sketch a hypothetical enterprise solution covering LLM selection, context engineering, multi-agent orchestration, security, deployment and integration. Be ready to explain alternatives and assumptions; this is a preparation exercise, not a stated interview task.
Document a PoC-to-pilot example
Prepare a concise account of a relevant PoC or pilot, including its success measures, what changed after evaluation and any reusable accelerator. The posting emphasizes pilots and measurable business outcomes.
Draft a responsible AI decision framework
Outline how you would assign governance responsibilities, assess compliance and ethical risks, and monitor an agent after deployment. Distinguish your proposed approach from any framework you have actually implemented.
Prepare framework and platform comparisons
Make a short comparison of how TOGAF or Zachman informs your architecture decisions, with examples from cloud AI platforms you have used. The posting names AWS, Azure and Google Cloud; avoid claiming experience you cannot substantiate.
Rehearse an executive briefing
Prepare a two-minute explanation of an AI architecture decision, its business impact and its risks, plus an example of mentoring architects or defining agent lifecycle, observability or interoperability standards if applicable. These capabilities appear among the posting’s differentiators.
Write targeted questions for Cognizant
Ask which business outcomes and enterprise integrations this architect would prioritize, how architecture and governance decisions are shared across teams, and how the role’s stated five-day in-office requirement in Chennai, Bangalore or Hyderabad relates to the page’s Hybrid label.

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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