The Role
Design and build LLM-based solutions and RAG pipelines, implement prompt engineering, develop Python-based backend APIs, integrate models with enterprise data, apply evaluation and guardrails, and collaborate cross-functionally to productionize GenAI applications.
Summary Generated by Built In
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)
EXL (NASDAQ: EXLS) is a leading data analytics and digital operations and solutions company. We partner with clients using a data and AI-led approach to reinvent business models, drive better business outcomes and unlock growth with speed. EXL harnesses the power of data, analytics, AI, and deep industry knowledge to transform operations for the world’s leading corporations in industries including insurance, healthcare, banking and financial services, media and retail, among others. EXL was founded in 1999 with the core values of innovation, collaboration, excellence, integrity and respect. We are headquartered in New York and have more than 54,000 employees spanning six continents. For more information, visit www.exlservice.com.
Skills Required
- 2-4 years experience in AI/ML, NLP, or Data Engineering projects
- Hands-on experience with LLMs and GenAI use cases (OpenAI, Claude, GPT)
- Experience building and optimising RAG pipelines, embeddings, and retrieval
- Experience with agentic AI concepts, agent orchestration and tool-calling architectures
- Proficiency in Python and PySpark for production-grade development and API integration
- Experience developing backend services/APIs using FastAPI, Flask or Streamlit
- Experience with data engineering platforms (Microsoft Fabric, Azure Databricks) and Snowflake
- Strong data analysis skills and experience handling large volumes of structured and unstructured data
- Familiarity with cloud platforms (Azure/AWS/GCP), SQL, containers, CI/CD and monitoring
- Prior experience in Data Engineering (ETL/ELT, pipelines, orchestration) or Data Science/ML lifecycle (NLP) or analytics engineering
- Experience with LangChain, LangGraph, or similar frameworks
- Exposure to fine-tuning / prompt tuning techniques (LoRA, PEFT)
- Experience with Azure OpenAI / Azure AI Search or similar enterprise AI stacks
- Awareness of enterprise AI considerations (data security, privacy, governance)
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The Company
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.
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