Associate AI Data Engineer

EXL

Early-career Generative AI / LLM Engineer building and deploying LLM-powered applications. Collaborating with data engineers and scientists for scalable GenAI solutions.

Posted 7/8/2026full-time🇮🇳 IndiaJuniorMid-LevelWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and developing LLM-based solutions, including RAG pipelines and prompt engineering techniques, while ensuring integration with enterprise systems and data sources. Proficient in Python frameworks for backend services and possesses strong data analysis skills for handling large volumes of data.

Highest-signal resume keywords

LLM DevelopmentRAG Pipeline OptimisationPython EngineeringData Engineering IntegrationCloud Platform Exposure

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

LLMRAGPythonPysparkSQLData AnalysisETLNLPPrompt EngineeringAPI Integration

Tools & Technologies

FastAPIFlaskStreamlitLangChainLangGraphAzure DatabricksSnowflakeAzure OpenAIAzure AI SearchCI/CD

Industry Keywords

AIMLData EngineeringData SecurityData PrivacyGovernanceAgentic AITool CallingAnalytics EngineeringEnterprise AI

Tech Stack

Tools & technologies

AWSAzureCloudETLFlaskGoogle Cloud PlatformPySparkPythonSQL

About the role

Key responsibilities & impact

Design and develop LLM-based solutions for business use cases (e.g., chatbots, summarisation, document intelligence)
Build and optimise RAG (Retrieval Augmented Generation) pipelines including data ingestion, embeddings, and retrieval
Implement prompt engineering techniques (prompt design, chaining, optimisation)
Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit)
Integrate LLM solutions with enterprise systems and structured/unstructured data sources
Apply basic guardrails and evaluation techniques to improve response quality and reduce hallucinations
Collaborate with cross-functional teams to ensure data quality, model performance, and deployment readiness
Document solutions and contribute to reusable components and best practices

Requirements

What you’ll need

2–4 years total experience, with exposure to AI/ML, NLP, or Data Engineering projects
Hands-on experience or strong learning exposure to LLM / GenAI use cases (projects, POCs, academic work, or professional)
Strong hands-on experience with LLMs (Claude, OpenAI, etc.)
RAG pipelines and retrieval optimisation
GPT + Agentic AI implementation experience
Experience with LangChain, LangGraph, or similar frameworks
Agent orchestration and tool-calling architectures
Deep understanding of LLM limitations, evaluation, and optimisation strategies
Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience
Deep data analysis experience and handling large volume of data
Fabric/Azure Databricks/Snowflake data engineering integration skills
Good exposure to Cloud platforms (Azure/AWS/GCP)
SQL
Containers, CI/CD, monitoring
Prior experience in one or more Data Engineering (ETL/ELT, pipelines, orchestration)
Data Science / ML lifecycle (especially NLP)
Analytics engineering / data products
Exposure to agentic workflows or tool calling concepts
Basic knowledge of fine-tuning / prompt tuning (LoRA, PEFT – optional exposure)
Experience with Azure OpenAI / Azure AI Search or similar stacks
Awareness of enterprise AI considerations (data security, privacy, governance)

Benefits

Comp & perks

N/A 📊 Check your resume score for this job Improve your chances of getting an interview by checking your resume score before you apply. Check Resume Score