New opportunity

AI Customer Engineer

Cognizant · Bangalore / Chennai, India

About this role

The AI Customer Engineer joins Cognizant’s AI Engineering team to build scalable AI agent solutions and production-grade Generative AI applications for enterprise adoption. The role works with architects, product teams and other stakeholders to deliver reliable, secure and high-performing systems.

Responsibilities

Build and deploy AI agents and GenAI-powered applications; design RAG pipelines, prompt-engineering 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 on the delivery of AI solutions.

Required qualifications

Hands-on experience building agents with Google ADK, Copilot Studio or another agent-development tool such as CrewAI or AutoGen. Ability to configure prompts, instructions, tools, reasoning and guardrails for agents.
Strong experience with 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 Python RESTful API development experience, including frameworks such as FastAPI and integration with third-party services, UI components and APIs.
Hands-on Docker deployments and GitHub-based code repositories and version control are mandatory. Understanding of microservices design and cloud-computing basics, plus strong communication skills, is required.

Preferred qualifications

End-to-end AI/GenAI project experience; exposure to enterprise-scale, production-grade AI deployments; ability to optimize model and service performance, scalability and reliability; cross-functional collaboration and adaptability to changing AI tools and standards.

Location and arrangement: Bangalore or Chennai, India. Despite a separate page label of “Hybrid,” the role description specifies work from office five days a week at a client or Cognizant office. The posting lists the position as full-time.

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 engineeringLLM integrationGoogle ADKCopilot StudioCrewAIAutoGenAI guardrailsVector databasesTokenizationPythonLangChainHugging FaceOpenAI APIRESTful APIsFastAPIDockerGitHubMicroservicesCloud computingCommunicationCross-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

Build deployable AI agents
The role centers on building and deploying AI agents and GenAI applications. The posting calls for hands-on agent development and configuration of prompts, instructions, tools, reasoning and guardrails.
Design RAG and LLM workflows
Designing RAG pipelines, prompt-engineering workflows and LLM integrations is an explicit duty. The requirements emphasize vector databases, tokenization and maintaining Python libraries.
Deliver Python services and integrations
The role develops scalable backend services and RESTful APIs, including integration with third-party services, UI components and APIs. Strong Python API development, such as with FastAPI, is required.
Support production-oriented deployment
The posting calls for cloud-native deployment using containers and microservices, and requires hands-on Docker deployment and GitHub version control. Performance, scalability and reliability optimization are preferred.
Coordinate delivery across teams
The engineer works with architects, product teams and other stakeholders to deliver reliable, secure and high-performing AI solutions. Communication and articulation are required.

Likely challenges to think through

Agent capability versus control
A likely challenge is giving agents useful tool access and reasoning while limiting unsafe or unreliable behavior; the posting requires guardrails but does not specify an approach. Prepare to explain how you would define tool boundaries, test failure cases and assess agent outputs.
Retrieval quality versus response cost
RAG design may require balancing relevant context against token use and latency; this is a preparation hypothesis, not a stated employer problem. Prepare to discuss how you would evaluate retrieval results, context selection and answer quality.
Prototype behavior versus service reliability
Turning an LLM workflow into a scalable API may expose trade-offs among latency, integration complexity and reliability. Prepare to walk through how you would package a Python service with Docker, handle dependency failures and measure performance; no particular architecture is specified.

Practice your answers

Technical practice

How would you structure an AI agent using Google ADK, Copilot Studio, CrewAI, or AutoGen?

What to coverThe posting names these as examples of agent-development tools. Suggested answer focus: explain how you would choose a tool, define the agent’s responsibilities, connect tools, and test its behavior without assuming one framework is required.

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

What to coverThe role explicitly calls for configuring these agent components. Suggested answer focus: describe how you would constrain tool use, handle unsafe or unsupported requests, and check whether the agent follows its instructions.

How would you design a RAG pipeline backed by a vector database?

What to coverThe posting requires strong experience with RAG, vector databases, and tokenization. Suggested answer focus: walk through document preparation, chunking, retrieval, context selection, and how token limits affect the design.

A RAG application returns plausible answers that are not supported by retrieved content. How would you diagnose it?

What to coverRAG pipelines and reliable AI applications are stated parts of the role. Suggested answer focus: inspect retrieval relevance, context passed to the model, prompt instructions, and evidence in the response; explain how you would compare changes rather than guessing at one cause.

How would you evaluate a prompt-engineering workflow and an LLM integration?

What to coverThe role includes prompt workflows and LLM integrations. Suggested answer focus: define representative test cases and discuss answer quality, consistency, latency, and cost as possible trade-offs.

How would you organize a maintainable Python library for reusable AI application components?

What to coverThe posting calls for creating and maintaining Python libraries and using options such as LangChain, Hugging Face, the OpenAI API, or local models. Suggested answer focus: discuss clear interfaces, dependency boundaries, testing, and what would change when switching a model or integration.

How would you design a Python REST API for an AI application that integrates third-party services and a UI?

What to coverStrong Python RESTful API development, frameworks such as FastAPI, and integration with services, UI components, and APIs are stated requirements. Suggested answer focus: cover request and response contracts, validation, error handling, and how the API would manage slow or failed dependencies.

How would you prepare an AI service for a Docker-based deployment while managing changes in GitHub?

What to coverThe posting explicitly requires hands-on Docker deployments and GitHub-based version control. Suggested answer focus: describe a reproducible container build, configuration separation, and a reviewable approach to code and deployment changes.

When would you separate AI application components into microservices rather than keep one backend service?

What to coverThe role calls for scalable backends, microservices design principles, and cloud-computing basics. Suggested answer focus: compare independent scaling and deployment against added network, operational, and debugging complexity.

How would you investigate and improve a production AI service that is slow or unreliable?

What to coverThe posting emphasizes production-grade systems and identifies performance, scalability, reliability, and security as delivery concerns. Suggested answer focus: isolate model, retrieval, API, and dependency delays; then explain how you would validate improvements without weakening safeguards.

Behavioural practice

Tell me about a time you aligned architects, product colleagues, and other stakeholders on an AI solution.

What to coverCross-functional delivery is central to the advertised role. Suggested answer focus: use a real situation, the actions you took to resolve differing priorities, and the resulting decision or delivery outcome.

Describe a time you explained a technical AI trade-off to a non-specialist stakeholder.

What to coverThe posting explicitly values communication and articulation. Suggested answer focus: give the situation, how you adapted your explanation and checked understanding, and what decision or outcome followed.

Tell me about an end-to-end AI or GenAI delivery challenge you worked through.

What to coverEnd-to-end project experience is listed as a preferred qualification. Suggested answer focus: identify the situation, your own actions across development and deployment, and the outcome, while distinguishing your contribution from the team’s.

Describe a time requirements or AI tools changed during delivery.

What to coverThe posting highlights adaptability to evolving tools, frameworks, and standards. Suggested answer focus: explain the change, how you evaluated and communicated a response, and the result.

Tell me about a time you addressed a reliability, performance, or security concern in an AI system.

What to coverDelivering reliable, secure, high-performing AI solutions is part of the role. Suggested answer focus: present the situation, the diagnostic and collaborative actions you took, and a measurable outcome if one is available.

Sharpen your knowledge

Topics to prepare

Agent design and guardrails
The posting requires knowledge of prompts, instructions, tools, reasoning and guardrails. Revise by sketching an agent’s decision flow and explaining when it should call a tool, decline a request or stop.
RAG, vector databases and tokenization
The posting explicitly calls for strong experience in all three. Trace a question from tokenization through retrieval and answer generation, then explain how poor retrieval could affect the answer.
API and integration design
The role develops scalable Python RESTful APIs that integrate third-party services, UI components and other APIs. Sketch request and response flows for a GenAI endpoint, including how it handles an upstream failure.
Microservices and cloud basics
The posting requires understanding of microservices design and cloud computing basics. Draw a containerized AI service with an API and model integration; explain service boundaries and a scaling trade-off.
Production AI trade-offs
Reliable, secure, high-performing delivery is a responsibility; optimization and end-to-end lifecycle experience are preferred. As preparation, compare two ways to improve latency or reliability and explain what you would measure.

Skills to revise

Configure an agent
The posting asks for hands-on agent development using Google ADK, Copilot Studio or an alternative such as CrewAI or AutoGen. In one of these tools, build a small agent with instructions, a tool and a guardrail; test both an allowed and a disallowed request.
Implement a RAG workflow
RAG pipelines, vector databases and prompt engineering are explicit requirements. Build a small retrieval-to-answer flow and inspect cases where the retrieved material is irrelevant; revise the prompt or retrieval setup and compare outputs.
Package Python and expose an API
The posting calls for maintaining Python libraries and developing RESTful APIs with frameworks such as FastAPI. Put reusable AI integration logic in a Python library, expose an endpoint and exercise its success and third-party-error paths.
Deploy with Docker and use GitHub
Docker-based deployments and GitHub version control are stated as mandatory. Containerize the API, run it locally, then practice committing a change and reviewing its diff in a GitHub repository.
Explain a cross-functional design
Strong communication and articulation are required, and the role works with architects, product teams and other stakeholders. Rehearse a brief explanation of an agent-and-RAG design, its integration points and one reliability trade-off for a mixed technical and product audience.

Your preparation checklist

Prepare an end-to-end project walkthrough
The role builds production-grade AI agents and GenAI applications. If you have a relevant project, prepare a brief walkthrough of the problem, architecture, your decisions, deployment, results and trade-offs. Otherwise, label the walkthrough as a hypothetical design.
Show how you configure an agent
Prepare an example using Google ADK, Copilot Studio, CrewAI, AutoGen or another agent tool. Explain the prompts, instructions, tools, reasoning setup and guardrails, including how you would test them.
Sketch a RAG pipeline
Draw a retrieval-to-response flow and be ready to explain vector database choices, tokenization, prompt construction and LLM integration. Include how you would check answer quality and handle weak retrieval as preparation suggestions.
Demonstrate Python API and library work
Select code you can discuss for a maintained Python library and a RESTful API, such as one built with FastAPI. Prepare to explain third-party, UI or API integrations, error handling and testing; distinguish work you have done from a proposed approach.
Walk through deployment and architecture
Have a Docker deployment example and GitHub repository history ready to discuss: both are stated requirements. Sketch how you would divide an AI application into services and deploy it in a cloud environment.
Prepare a reliability and collaboration example
The posting emphasizes secure, reliable, high-performing solutions and work with architects, product teams and other stakeholders. Prepare one concise example, if available, showing a trade-off you communicated and its outcome; otherwise outline how you would approach that discussion.
Write questions for the employer
Ask which agent platforms and model options the team uses, how it evaluates production reliability, and how engineering and product stakeholders share decisions. Confirm the work arrangement: the role text specifies five office days per week in Bangalore or Chennai despite a separate 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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