New opportunity

AI Data Engineer

EXL Talent Acquisition Team · Pune, India

About this role

EXL Talent Acquisition Team seeks an AI Data Engineer in Pune, Maharashtra, India to build LLM-based applications and supporting data pipelines for business use cases. The role combines generative AI development, agentic workflows, backend engineering and enterprise data integration.

Responsibilities

Design single-agent or simple multi-agent LLM applications. Build and maintain RAG pipelines covering ingestion, chunking, embeddings, retrieval and response generation.
Implement prompt templates, chaining and basic tool or function calling. Develop Python-based backend services and APIs using FastAPI, Flask or Streamlit, and integrate solutions with enterprise systems, databases and APIs.
Apply guardrails and validation checks to improve response quality and reduce hallucinations. Work with Data Engineering teams on data quality, pipeline efficiency and documentation, and with MLOps teams on deployment, monitoring and iterative improvement. Document reusable components and best practices.

Required qualifications

The qualifications specify 4–6 years of total experience, including 1+ year of hands-on GenAI or LLM application experience; the Job Info section separately lists 3–6 years.
Hands-on experience with LLMs such as Claude or OpenAI, RAG and retrieval optimization, GPT and agentic AI implementation, and LangChain, LangGraph or similar frameworks. Understand agent orchestration, tool calling, LLM limitations, evaluation and optimization.
Production-grade Python and PySpark engineering, API integration, analysis of large data volumes, and data engineering integration with Fabric, Azure Databricks or Snowflake. Exposure to Azure, AWS or GCP, SQL, containers, CI/CD and monitoring.
Prior experience in at least one of data engineering (ETL/ELT, pipelines and orchestration), data science or the ML lifecycle (especially NLP), or analytics engineering and data products.

Preferred qualifications include model fine-tuning with LoRA or PEFT, prompt optimization, evaluating LLM output quality, relevance and latency, enterprise GenAI data privacy and security, Azure AI, Azure OpenAI or AI Search, and client-facing AI solutions or proofs of concept.

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

Skills for this role

Generative AILarge Language ModelsClaudeOpenAIGPTRAGRetrieval optimizationEmbeddingsPrompt engineeringAgentic AIAgent orchestrationTool callingLangChainLangGraphPythonPySparkFastAPIFlaskStreamlitAPI integrationData engineeringData analysisMicrosoft FabricAzure DatabricksSnowflakeAzureAWSGoogle Cloud PlatformSQLContainers/Containerization (general)

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 end-to-end RAG pipelines
The role explicitly covers ingestion, chunking, embeddings, retrieval and response generation, with retrieval optimisation. Prepare to explain how choices at each stage affect the final response.
Design focused LLM and agent workflows
The posting calls for single-agent or simple multi-agent applications, prompt templates, chaining and basic tool calling. Be ready to explain when a simpler workflow is sufficient and how tools are orchestrated.
Deliver production-grade backend integrations
Python and PySpark engineering, AI application APIs, and integration with enterprise systems and databases are central requirements. Focus preparation on service design and handling large data volumes.
Connect AI applications to data platforms
The role requires data-engineering integration with Fabric, Azure Databricks or Snowflake and collaboration on data quality and pipeline efficiency. Prepare to trace how source data reaches and supports an application.
Improve quality and maintainability after deployment
Responsibilities include guardrails and validation to reduce hallucinations, collaboration with MLOps on monitoring and iteration, and documentation of reusable components. LLM evaluation and optimisation are also required knowledge.

Likely challenges to think through

Balancing retrieval quality and pipeline efficiency
Preparation hypothesis, not a stated employer problem: chunking and retrieval changes may improve relevance while increasing processing or response time. Prepare a concrete way to compare retrieval results, answer quality and efficiency.
Keeping agent behavior reliable without excess complexity
Preparation hypothesis: adding agents and tools can expand what an application does but make failures harder to trace. Prepare to justify single-agent versus simple multi-agent choices and describe validation at tool and response boundaries.
Moving from a working application to an operable service
Preparation hypothesis: enterprise integrations can make deployment and changes harder to coordinate. Prepare to explain how you would document interfaces, check data quality, and work with Data Engineering and MLOps on monitoring and iterative improvements.

Practice your answers

Technical practice

How would you design a RAG pipeline from ingestion through response generation?

What to coverThe posting explicitly covers ingestion, chunking, embeddings, retrieval and response generation. Preparation suggestion: explain how data quality, chunk boundaries and source updates affect the answer, and how you would check each stage.

How would you diagnose and improve poor retrieval results?

What to coverRetrieval optimisation is a stated skill. Preparation suggestion: distinguish missing or stale source data from chunking or ranking problems, then describe how you would test whether a change improves relevance.

When would you use prompt templates and chaining in an LLM application?

What to coverThe role calls for prompt templates and chaining. Preparation suggestion: explain how you would keep inputs and outputs consistent, and weigh a simpler prompt against a multi-step chain.

When would you choose a single agent over a simple multi-agent workflow?

What to coverThe posting names both patterns, along with agent orchestration and tool calling. Preparation suggestion: discuss task decomposition, tool-result validation, and the added latency and failure points of multiple agents.

How would you build a Python API that connects an LLM application to enterprise systems?

What to coverPython backend services, APIs, databases and enterprise integration are stated responsibilities. Preparation suggestion: discuss request validation, integration failures and testing, using FastAPI, Flask or Streamlit only where its capabilities fit the service.

How would you reduce hallucinations and protect enterprise data in an LLM application?

What to coverBasic guardrails and validation are responsibilities; enterprise GenAI privacy and security are preferred knowledge. Preparation suggestion: distinguish checking an answer against retrieved evidence from controlling what data is sent to a model or tool.

How would you evaluate an LLM application before and after an improvement?

What to coverThe posting calls for understanding LLM limitations and evaluation, and prefers experience assessing output quality, relevance and latency. Preparation suggestion: describe representative test cases, measurable criteria and the trade-offs between answer quality and response time.

How would you prepare large-volume enterprise data for an AI pipeline?

What to coverProduction-grade Python and PySpark, large-volume analysis, data quality and integration with Fabric, Azure Databricks or Snowflake appear in the posting. Preparation suggestion: explain how you would validate records, address inefficient processing and document the data pipeline without assuming a particular platform is mandatory.

What would you monitor after deploying an LLM-backed service?

What to coverThe role involves MLOps collaboration on deployment, monitoring and iterative improvement, with exposure to containers and CI/CD. Preparation suggestion: connect operational signals such as failures and latency with checks of retrieval and response quality, and explain how findings would guide a change.

When might prompt optimisation be preferable to model fine-tuning?

What to coverPrompt optimisation and exposure to LoRA or PEFT fine-tuning are listed as preferred, not required. Preparation suggestion: compare expected quality gains with data, maintenance and evaluation costs, and state what evidence would justify moving beyond prompts.

Behavioural practice

Tell me about a time you resolved a data-quality issue with a data engineering team.

What to coverThe role explicitly involves working with Data Engineering on data quality and pipeline efficiency. Preparation suggestion: use situation, action and result to show how you identified the issue, coordinated a fix and verified its effect.

Describe a time you worked with an operations or MLOps team to improve a deployed service.

What to coverCollaboration with MLOps on deployment, monitoring and iterative improvement is a stated duty. Preparation suggestion: give a real situation, your actions across team boundaries and the observed result; do not assume a particular deployment setup.

Tell me about a time you explained an AI solution or proof of concept to a client.

What to coverClient-facing AI solutions or proofs of concept are preferred experience. Preparation suggestion: if applicable, use situation, action and result to show how you clarified the use case, communicated limitations and responded to feedback.

Describe a time you made a technical component easier for others to reuse.

What to coverDocumenting solutions, reusable components and best practices is part of the role. Preparation suggestion: give the situation, what you documented or changed, and a concrete result for other engineers.

Tell me about a time an application result fell short of expectations and you improved it.

What to coverThe posting emphasises LLM evaluation, response quality and iterative improvement. Preparation suggestion: use situation, action and result to explain how you established the problem, tested a change and judged whether it helped.

Sharpen your knowledge

Topics to prepare

RAG design and retrieval optimization
The posting requires ingestion, chunking, embeddings, retrieval and response generation. Exercise: diagram that flow and explain how you would compare two chunking approaches and diagnose irrelevant retrieved passages.
Agent orchestration and tool calling
The role calls for single-agent or simple multi-agent applications, prompt chaining and tool/function calling. Exercise: sketch a single-agent workflow that calls an enterprise API, then explain when a second agent would add value rather than complexity.
LLM limitations, evaluation and guardrails
The posting requires understanding LLM limitations and applying validation checks to reduce hallucinations. Exercise: define a small test set with unsupported-answer cases; explain what you would check in retrieved evidence and generated responses. Output quality, relevance and latency evaluation are also listed as preferred.
Data and AI foundations at scale
The role requires large-volume data analysis and prior experience in at least one of data engineering, data science/ML lifecycle or analytics engineering. Exercise: trace how poor source-data quality would affect an ingestion pipeline and downstream RAG answers, then identify where to measure and correct it.
Deployment and integration concepts
The posting mentions enterprise databases and APIs, collaboration with MLOps on deployment and monitoring, and exposure to cloud platforms, containers and CI/CD. Exercise: draw the boundaries between a Python AI service, its data source and monitoring, and explain what each boundary needs to validate.

Skills to revise

Build a small end-to-end RAG pipeline
Practice ingesting sample documents, chunking them, generating embeddings, retrieving passages and producing an answer. Inspect a failed retrieval and adjust the retrieval approach; these stages and retrieval optimization are explicit responsibilities.
Implement a bounded agent workflow
Using LangChain, LangGraph or a similar framework named by the posting, practice a prompt template, a short chain and one tool call. Test what happens when the tool returns an error; agent orchestration and tool calling are required.
Develop a Python AI service
Practice exposing a model-backed operation through a Python backend using FastAPI or Flask, including input validation and an API integration. The role explicitly requires production-grade Python, backend services and integration with enterprise APIs.
Prepare and inspect data with PySpark
Practice cleaning and analyzing a large sample dataset with PySpark, checking data quality and documenting the transformation. Explain how its output could feed a pipeline integrated with Fabric, Azure Databricks or Snowflake; PySpark, large-volume analysis and those integration skills are listed.
Test responses and operational signals
Create a small set of answer-quality and hallucination checks, record failures, and specify what you would monitor after deployment. Guardrails, validation, monitoring and iterative improvement are responsibilities; this is a suggested practice exercise, not a specified employer test.

Your preparation checklist

Map your experience to the role
Prepare a one-page evidence sheet with dates, your contributions and outcomes for LLM applications, production Python/PySpark, API integration and at least one stated data or ML foundation. The qualifications specify 4–6 years total and 1+ year hands-on GenAI experience; Job Info separately says 3–6 years.
Walk through a RAG application
Prepare a project walkthrough showing ingestion, chunking, embeddings, retrieval and response generation. Include a retrieval optimisation decision, a result you measured and what you would change next; use an actual project where possible.
Sketch a simple agent design
For interview practice, draw a single-agent or simple multi-agent flow with prompt templates, tool or function calls and failure handling. Be ready to explain when a simpler RAG flow would suffice and where LangChain, LangGraph or a similar framework fits.
Show the engineering behind the AI service
Prepare an architecture diagram or code excerpt covering a Python API, database or enterprise API integration, and a data pipeline. Explain relevant PySpark, SQL and large-volume data decisions using work you have actually done.
Prepare an evaluation and guardrails example
Bring a small test-case table showing how you would assess answer quality, relevance, latency and hallucination risks. Identify validation checks and LLM limitations; label any proposed tests as interview design rather than past results.
Explain the deployment handoff
Outline how you would document reusable components and coordinate with Data Engineering on data quality and pipeline efficiency and with MLOps on deployment, monitoring and iteration. Ground claims about cloud, containers or CI/CD in your own experience.
Write targeted questions for the employer
Ask which business use cases and data platforms the Pune role prioritises, how RAG quality is evaluated, and how Data Engineering and MLOps responsibilities are divided. Clarify the differing experience ranges in the qualifications and Job Info.

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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