Technical practice
Suggested practice: How would you design an AI agent to use tools to complete an enterprise task?
What to coverThe role calls for building agents with platforms such as Google ADK, Copilot Studio, CrewAI or AutoGen. Suggested preparation: explain how you would define instructions, select tools, manage reasoning steps and decide when the agent should stop or seek clarification.
Suggested practice: How would you configure and test prompts and guardrails for an agent?
What to coverThe posting explicitly names prompts, instructions and guardrails as agent-development concepts. Suggested preparation: discuss how you would test intended behavior, unsafe or out-of-scope requests, and the trade-off between restricting responses and completing useful tasks.
Suggested practice: How would you build and evaluate a RAG pipeline?
What to coverDesigning RAG pipelines is a stated responsibility. Suggested preparation: walk through preparing content, retrieving context and generating an answer, then explain how you would investigate irrelevant retrieval or answers unsupported by the retrieved material.
Suggested practice: How do tokenization and vector-database choices affect retrieval?
What to coverThe posting calls for experience with tokenization and vector databases. Suggested preparation: explain how input length, chunk boundaries and retrieval choices can affect context quality, cost and latency.
Suggested practice: How would you choose and integrate an LLM for an application?
What to coverThe role includes LLM integrations and mentions the OpenAI API, Hugging Face and local models. Suggested preparation: compare integration approaches in terms of capability, latency, operational effort and application constraints without assuming Cognizant uses a particular model.
Suggested practice: How would you structure a reusable Python library for AI application components?
What to coverCreating and maintaining Python libraries is listed among the required capabilities, alongside use of tools such as LangChain. Suggested preparation: describe clear interfaces, dependency management and tests that would let backend services reuse an LLM or retrieval component.
Suggested practice: How would you design a Python REST API that connects an AI service to other systems?
What to coverThe posting emphasizes RESTful Python APIs, frameworks such as FastAPI, and integrations with third-party services, UI components and other APIs. Suggested preparation: address request validation, response contracts, integration failures and how the API would remain responsive as demand grows.
Suggested practice: How would you package and version-control an AI backend for deployment?
What to coverHands-on Docker deployments and GitHub source control are explicitly required. Suggested preparation: explain container configuration, reproducible dependencies and a Git-based workflow for reviewing and tracking changes; do not assume a specific deployment pipeline.
Suggested practice: When would you separate an AI application into microservices?
What to coverThe role calls for cloud-native deployment and an understanding of microservices principles and cloud basics. Suggested preparation: compare service boundaries with a simpler architecture, including the effects on scaling, communication, reliability and operational complexity.
Suggested practice: How would you diagnose slow or unreliable behavior in a production-grade AI application?
What to coverThe posting emphasizes reliable, high-performing solutions and lists performance, scalability and reliability optimization as preferred strengths. Suggested preparation: explain how you would isolate model, retrieval, API and integration bottlenecks, then evaluate improvements against response quality and operational cost.
Behavioural practice
Suggested practice: Tell me about working across engineering and product teams to deliver a solution.
What to coverThe role involves architects, product teams and other cross-functional stakeholders. Suggested preparation: use a real situation, your actions to align requirements or resolve a disagreement, and the resulting delivery outcome.
Suggested practice: Describe a time you explained a complex technical trade-off to a stakeholder.
What to coverStrong communication and articulation skills are explicitly requested. Suggested preparation: give the situation, how you made the options understandable and actionable, and what decision or result followed.
Suggested practice: Tell me about taking an AI or software solution from initial work toward deployment.
What to coverEnd-to-end AI/GenAI lifecycle experience is listed as a preferred strength. Suggested preparation: choose a real example, distinguish your own actions at each stage, and describe the outcome without implying ownership you did not have.
Suggested practice: Describe a time you improved the reliability or performance of a deployed service.
What to coverThe posting emphasizes reliable, high-performing production-grade AI solutions. Suggested preparation: state the observed problem, how you investigated and prioritized changes, and the measurable or observable result.
Suggested practice: Tell me about adapting when an AI tool or technical approach changed.
What to coverAdaptability to evolving AI tools, frameworks and standards is listed as a preferred strength. Suggested preparation: explain the situation, how you evaluated and communicated the change, and what happened after you applied your decision.