Key Responsibilities
- 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.
Must-Have Skills
Experience
- 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)
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
- 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)
Key Responsibilities
- 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.
Must-Have Skills
Experience
- 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)
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
- 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)
Key Responsibilities
- 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.
Must-Have Skills
Experience
- 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)
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
- 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)l
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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.
