Key Responsibilities
- Design and develop LLM-based applications using single-agent or simple multi-agent patterns for business use cases
- Build and maintain RAG pipelines: data ingestion → chunking → embeddings → retrieval → response generation
- Implement prompt engineering techniques (prompt templates, chaining, basic tool/function calling)
- Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit)
- Integrate AI solutions with enterprise systems, databases, and APIs
- Apply basic guardrails and validation checks to improve response quality and reduce hallucination
- Work with Data Engineering teams to ensure data quality, pipeline efficiency, and proper documentation
- Collaborate with MLOps teams for deployment, monitoring, and iterative improvements
- Document solutions, reusable components, and best practices
Must-Have Skills
Experience
- 4–6 years total experience, with 1+ year hands-on experience in GenAI / LLM-based applications
LLM / GenAI & Agentic Engineering
- 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
Core Engineering
- 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
Data / AI Foundations (Mandatory)
Prior experience in one or more:
- Data Engineering (ETL/ELT, pipelines, orchestration)
- Data Science / ML lifecycle (especially NLP)
- Analytics engineering / data products
Good-to-Have / Preferred
- Exposure to model fine-tuning (LoRA/PEFT) or prompt optimisation techniques
- Experience with evaluation of LLM outputs (quality, relevance, latency)
- Understanding of enterprise data privacy and security considerations in GenAI
- Exposure to Azure AI / Azure OpenAI / AI Search ecosystems
- Experience working on real client-facing AI solutions or POCs
Key Responsibilities
- Design and develop LLM-based applications using single-agent or simple multi-agent patterns for business use cases
- Build and maintain RAG pipelines: data ingestion → chunking → embeddings → retrieval → response generation
- Implement prompt engineering techniques (prompt templates, chaining, basic tool/function calling)
- Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit)
- Integrate AI solutions with enterprise systems, databases, and APIs
- Apply basic guardrails and validation checks to improve response quality and reduce hallucination
- Work with Data Engineering teams to ensure data quality, pipeline efficiency, and proper documentation
- Collaborate with MLOps teams for deployment, monitoring, and iterative improvements
- Document solutions, reusable components, and best practices
Must-Have Skills
Experience
- 4–6 years total experience, with 1+ year hands-on experience in GenAI / LLM-based applications
LLM / GenAI & Agentic Engineering
- 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
Core Engineering
- 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
Data / AI Foundations (Mandatory)
Prior experience in one or more:
- Data Engineering (ETL/ELT, pipelines, orchestration)
- Data Science / ML lifecycle (especially NLP)
- Analytics engineering / data products
Good-to-Have / Preferred
- Exposure to model fine-tuning (LoRA/PEFT) or prompt optimisation techniques
- Experience with evaluation of LLM outputs (quality, relevance, latency)
- Understanding of enterprise data privacy and security considerations in GenAI
- Exposure to Azure AI / Azure OpenAI / AI Search ecosystems
- Experience working on real client-facing AI solutions or POCs
Key Responsibilities
- Design and develop LLM-based applications using single-agent or simple multi-agent patterns for business use cases
- Build and maintain RAG pipelines: data ingestion → chunking → embeddings → retrieval → response generation
- Implement prompt engineering techniques (prompt templates, chaining, basic tool/function calling)
- Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit)
- Integrate AI solutions with enterprise systems, databases, and APIs
- Apply basic guardrails and validation checks to improve response quality and reduce hallucination
- Work with Data Engineering teams to ensure data quality, pipeline efficiency, and proper documentation
- Collaborate with MLOps teams for deployment, monitoring, and iterative improvements
- Document solutions, reusable components, and best practices
Must-Have Skills
Experience
- 4–6 years total experience, with 1+ year hands-on experience in GenAI / LLM-based applications
LLM / GenAI & Agentic Engineering
- 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
Core Engineering
- 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
Data / AI Foundations (Mandatory)
Prior experience in one or more:
- Data Engineering (ETL/ELT, pipelines, orchestration)
- Data Science / ML lifecycle (especially NLP)
- Analytics engineering / data products
Good-to-Have / Preferred
- Exposure to model fine-tuning (LoRA/PEFT) or prompt optimisation techniques
- Experience with evaluation of LLM outputs (quality, relevance, latency)
- Understanding of enterprise data privacy and security considerations in GenAI
- Exposure to Azure AI / Azure OpenAI / AI Search ecosystems
- Experience working on real client-facing AI solutions or POCs
Skills Required
- 4-6 years total experience with 1+ year hands-on GenAI/LLM-based application experience
- Hands-on experience with LLMs (OpenAI, Claude, GPT)
- Design and maintain RAG pipelines (ingestion, chunking, embeddings, retrieval, response generation)
- Experience implementing GPT + agentic AI, agent orchestration, and tool-calling architectures
- Experience with LangChain, LangGraph or similar frameworks
- Strong Python and PySpark production-grade engineering skills and API integration experience
- Develop backend services/APIs using FastAPI, Flask, or Streamlit
- Data engineering integration experience with Fabric/Azure Databricks/Snowflake
- Experience with cloud platforms (Azure/AWS/GCP), SQL, containers, CI/CD and monitoring
- Prior experience in one or more: Data Engineering (ETL/ELT/pipelines), Data Science/ML lifecycle (especially NLP), or Analytics engineering/data products
- Implement prompt engineering techniques, basic tool/function calling, and apply guardrails/validation to reduce hallucination
- Collaborate with Data Engineering and MLOps teams for deployment, monitoring, and iterative improvements
- Exposure to model fine-tuning (LoRA/PEFT) or prompt optimization techniques
- Experience evaluating LLM outputs for quality, relevance, and latency
- Understanding of enterprise data privacy and security considerations in GenAI
- Exposure to Azure AI / Azure OpenAI / AI Search ecosystems and client-facing AI solutions/POCs
What We Do
Choosing a digital partner is about more than capabilities — it’s about collaboration and character. Unrealistic overhauls and off-the-shelf products ignore what matters most — your unique needs, culture, goals, and your legacy data and technology environments. At EXL, our collaboration is built on ongoing listening and learning to adapt our methodologies. We’re your business evolution partner—tailoring solutions that make the most of data to make better business decisions and drive more intelligence into your increasingly digital operations. Whether your goals are scaling the use of AI and digital, redesign operating models, or driving better and faster decisions, we’re here to partner with you to help you gain—and maintain—competitive advantage with efficient, sustainable models at scale. Our expertise in transformation, data science, and change management helps make your business more efficient and effective, improve customer relationships and enhance revenue growth. Instead of focusing on multi-year, resource- and time-intensive platform designs or migrations, we look deeper at your entire value chain to integrate strategies with impact. We use our specialization in analytics, digital interventions, and operations management—alongside deep industry expertise — to deliver solutions that help you outperform the competition. At EXL, it’s all about outcomes—your outcomes—and delivering success on your terms. Share your goals with us and together, we’ll optimize how you leverage data to drive your business forward. For more information, visit www.exlservice.com.
