New opportunity

AI Customer Engineer

Cognizant · Bangalore / Chennai, India

About this role

The AI Customer Engineer builds scalable AI agent solutions and helps enterprises adopt generative AI. Working with architects, product teams and other stakeholders, this member of Cognizant’s AI Engineering team delivers production-grade AI applications.

Responsibilities

Build and deploy AI agents and generative AI applications; design RAG pipelines, prompt workflows and LLM integrations.
Develop scalable backend services and APIs for AI systems, and support cloud-native deployments using containers and microservices.
Collaborate across teams to deliver reliable, secure and high-performing solutions.

Required qualifications

Hands-on experience building agents with Google ADK, Copilot Studio or tools such as CrewAI or AutoGen; ability to configure prompts, instructions, tools, reasoning and guardrails.
Strong knowledge of RAG pipelines, vector databases, tokenization and prompt engineering. Experience creating and maintaining Python libraries and using LangChain, Hugging Face, the OpenAI API or local models.
Strong experience developing RESTful APIs in Python with frameworks such as FastAPI and integrating third-party services, UI components and APIs.
Hands-on Docker deployment and GitHub repository and version-control experience are mandatory. Understanding of microservices design and cloud computing basics, plus strong communication and articulation skills, is required.

Preferred qualifications

End-to-end AI or generative AI project experience; exposure to enterprise-scale, production-grade deployments; ability to improve model and service performance, scalability and reliability; collaboration across engineering, product and business teams; and adaptability as AI tools and standards evolve.

The position requires working five days a week at a client or Cognizant office in Chennai or Bangalore. Although the page labels the work model Hybrid, the role-specific requirements specify on-site work; arrangements may change with project and business needs.

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

Skills for this role

AI agentsGenerative AIRAGPrompt engineeringLarge language modelsGoogle ADKCopilot StudioCrewAIAutoGenAgent guardrailsVector databasesTokenizationPythonLangChainHugging FaceOpenAI APIFastAPIRESTful APIsDockerGitHubMicroservicesCloud computingBackend developmentCommunicationCross-functional collaboration

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

Deliver deployable AI agents
The role builds and deploys AI agents and GenAI applications. The posting calls for configuring prompts, instructions, tools, reasoning and guardrails using an agent framework such as Google ADK, Copilot Studio, CrewAI or AutoGen. Prepare to explain the design and deployment decisions in an agent implementation.
Design RAG and LLM workflows
The role explicitly includes RAG pipelines, prompt workflows and LLM integrations, with strong knowledge of vector databases and tokenization. Prepare to trace how a request moves from retrieval through prompt construction to a model response.
Build and ship Python services
The posting emphasizes scalable RESTful APIs in Python, third-party and UI integration, Docker deployments, GitHub version control, microservices principles and cloud basics. Prepare to explain how these pieces fit into a deployable service.
Deliver production-grade solutions across teams
The role works with architects, product teams and other stakeholders to deliver reliable, secure, high-performing AI applications. Prepare to communicate technical trade-offs clearly to both engineering and non-engineering partners; enterprise-scale experience is preferred, not stated as required.

Likely challenges to think through

Agent flexibility versus control
Preparation hypothesis: giving an agent useful tools and reasoning freedom can make its behavior harder to constrain. Prepare to explain where you would place guardrails, how you would limit tool access and how you would check whether the agent follows its instructions.
Retrieval quality versus response cost
Preparation hypothesis: retrieving more context may improve coverage but increase token use, latency or irrelevant material. Prepare to discuss how you would assess retrieval relevance and choose what context reaches the LLM.
Integration speed versus service reliability
Preparation hypothesis: connecting models, third-party services and UI-facing APIs can speed delivery while adding failure points. Prepare to explain API boundaries, error handling and what you would verify before a Docker-based deployment.

Practice your answers

Technical practice

How would you choose and structure a framework for an AI agent?

What to coverThe role calls for hands-on agent development with Google ADK, Copilot Studio, or tools such as CrewAI or AutoGen. Preparation suggestion: Explain how you would choose among familiar tools based on the agent’s task, integrations, and deployment constraints, without claiming one is required for every solution.

How would you configure an agent’s instructions, tools, reasoning, and guardrails?

What to coverThe posting explicitly names these agent-development concepts. Preparation suggestion: Walk through tool selection and permission boundaries, how instructions shape behavior, and how you would test responses and tool use against the intended task.

How would you design and troubleshoot a RAG pipeline?

What to coverThe role includes RAG pipelines and vector databases. Preparation suggestion: Cover document preparation, retrieval, context assembly, and generation; distinguish retrieval failures from generation failures when diagnosing a poor answer.

How do tokenization and prompt design affect an LLM workflow?

What to coverThe posting requires tokenization and prompt engineering knowledge. Preparation suggestion: Discuss context limits, how you would allocate space between instructions and retrieved material, and the trade-off between more context and latency or cost.

How would you integrate an LLM into a maintainable Python application?

What to coverThe role mentions Python libraries and the use of LangChain, Hugging Face, the OpenAI API, or local models. Preparation suggestion: Describe clear interfaces, dependency management, and how you would evaluate an API-based versus local-model integration for a given application.

How would you design a Python REST API for an AI application?

What to coverThe posting emphasizes RESTful APIs using frameworks such as FastAPI, including third-party service and UI integration. Preparation suggestion: Address request and response contracts, input validation, error handling, and how clients would receive useful results or failures.

What would you check when deploying an AI service with Docker and GitHub?

What to coverHands-on Docker deployment and GitHub version control are stated requirements. Preparation suggestion: Explain reproducible builds, configuration outside the image, and how you would trace a deployed version to its repository changes when troubleshooting.

When would you separate AI application components into microservices?

What to coverThe role calls for microservices design principles and cloud computing basics in cloud-native deployments. Preparation suggestion: Compare separation of responsibilities and independent scaling with the added complexity of network calls, deployment, and operations.

How would you diagnose a slow or unreliable production AI service?

What to coverThe posting calls for reliable, high-performing solutions and highlights performance, scalability, and reliability optimization. Preparation suggestion: Identify where time or failures occur across the API, retrieval, model, and dependencies, then explain how you would measure whether a change helped.

How would you evaluate the security and quality of an AI application before deployment?

What to coverThe role calls for secure, production-grade AI solutions. Preparation suggestion: Describe testing groundedness and failure cases, limiting access to data and tools, and weighing safeguards against usability; these are preparation topics, not stated employer procedures.

Behavioural practice

Tell me about a time you aligned engineering, product, and business stakeholders.

What to coverThe role involves collaboration with architects, product teams, and other stakeholders. Preparation suggestion: Use a real situation, describe how you resolved differing priorities, and state the outcome and your contribution.

Describe a time you explained a complex technical trade-off to a nontechnical stakeholder.

What to coverStrong communication and articulation skills are listed for the role. Preparation suggestion: Give the situation, how you made the options understandable, and what decision or result followed.

Tell me about your involvement in delivering an AI application end to end.

What to coverEnd-to-end AI or GenAI project experience is listed as a preferred qualification. Preparation suggestion: If you have a relevant example, specify the situation, your actions across development and deployment, and the observed result without overstating ownership.

Describe a time you improved the reliability or performance of a deployed service.

What to coverThe posting highlights production-grade systems and optimization of performance, scalability, and reliability. Preparation suggestion: State the initial problem, the evidence you used to choose an action, and the measurable or observable result.

Tell me about a time you adapted to a changing AI tool or technical standard.

What to coverAdaptability as AI tools, frameworks, and standards evolve is a preferred qualification. Preparation suggestion: Describe what changed, how you evaluated and applied the change, and what the team or project gained or learned.

Sharpen your knowledge

Topics to prepare

Agent design and guardrails
Review how prompts, instructions, tools, reasoning and guardrails shape an agent’s behavior. Exercise: sketch an agent workflow and explain when it may call a tool, refuse a request or ask for clarification. These concepts are explicit requirements for the role.
RAG and tokenization
Review retrieval, vector-database indexing, context selection and token limits. Exercise: draw a RAG pipeline and explain how you would diagnose an irrelevant retrieved passage or an answer that exceeds its context budget. The posting explicitly calls for strong RAG, vector database and tokenization knowledge.
REST services and system boundaries
Review RESTful API design, microservices principles and cloud-computing basics. Exercise: diagram the boundary between an AI service, a third-party API and a UI, then explain how requests and errors cross it. The role develops scalable Python APIs and calls for understanding microservices and cloud basics.
Production trade-offs
Review the trade-offs among security, latency, scalability and reliability in an AI application. Exercise: propose how to investigate a slow or unreliable agent endpoint and explain which trade-off you would address first. Reliable, secure, high-performing delivery is a responsibility; optimization and enterprise-scale exposure are preferred qualifications.

Skills to revise

Configure an agent
Practice building a small agent with one of Google ADK, Copilot Studio, CrewAI or AutoGen. Define its instructions, one tool and a guardrail, then test a normal request and a request the guardrail should constrain. Hands-on agent development with these tools or comparable listed options is required.
Implement a RAG workflow
Practice indexing a small document set in a vector database, retrieving passages and passing them to a prompted LLM response. Inspect one poor answer and adjust retrieval or the prompt. This rehearses the RAG and prompt workflows the role builds.
Package Python LLM integration
Practice creating a small reusable Python library that wraps an LLM integration using one of the listed options, such as the OpenAI API or a local model. Add a usage example and revise its interface after a change. The posting explicitly mentions creating and maintaining Python libraries and using listed model tools or services.
Build a Python REST API
Practice exposing an AI operation through a FastAPI REST endpoint, including input validation, error responses and a call to a third-party service or API. Explain how a UI would consume the endpoint. The posting emphasizes Python REST APIs and integration with external services and UI components.
Deploy with Docker and track changes in GitHub
Practice containerizing the Python API, running it locally, and committing the Docker configuration and application changes to a GitHub repository. Rebuild after a code change. Hands-on Docker deployment and GitHub version control are explicitly marked as mandatory.
Articulate a cross-functional design
Practice a brief explanation of an agent-and-API design for an architect, a product stakeholder and an engineer, adjusting the emphasis while keeping the same technical facts. Strong communication is required, and the role works across those teams.

Your preparation checklist

Prepare an end-to-end project walkthrough
The role values end-to-end AI work. Choose a real project, if available, and outline the problem, your contributions, architecture, deployment, results and trade-offs. Label any practice example as hypothetical.
Demonstrate agent configuration decisions
The posting asks for hands-on agent experience with Google ADK, Copilot Studio or tools such as CrewAI or AutoGen. Prepare a concrete example showing how you configured prompts, instructions, tools, reasoning and guardrails.
Sketch a RAG design
For a practice design exercise, draw document ingestion, retrieval, a vector database and LLM integration. Be ready to explain tokenization, prompt choices, failure cases and how you would evaluate answers; this is preparation, not a stated interview task.
Gather Python and API evidence
Identify code or examples that show a maintained Python library and a RESTful API, such as one built with FastAPI. Prepare to explain third-party or UI integration choices and your use of a listed model tool or API.
Rehearse the deployment walkthrough
Docker-based deployment and GitHub version control are explicitly required. Prepare a repository and containerization walkthrough, then explain how you would apply microservices and cloud basics to a scalable backend.
Explain production and collaboration trade-offs
Prepare one concise account of how you worked with engineering, product or business stakeholders. Distinguish measured outcomes from proposed approaches to security, performance, scalability and reliability.
Bring role-specific questions
Ask which agent platforms and deployment environments the team uses, how production quality is assessed, and how work is divided with architects and product teams. Clarify the role-specific five-day onsite requirement in Chennai or Bangalore, despite the page’s Hybrid label.

Availability last checked: 5 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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