Design and build production GenAI systems: LLM/agentic applications, end-to-end RAG pipelines, prompt engineering, APIs, and integrations with enterprise data platforms. Apply guardrails, evaluation, and collaborate with Data Engineering and MLOps to deploy, monitor, and scale reusable components and best practices.
Key Responsibilities
- Design and develop LLM-powered applications using agentic patterns (single/multi-agent) for business use cases
- Build and optimise end-to-end RAG pipelines (ingestion, embeddings, retrieval, orchestration, response synthesis)
- Implement prompt engineering and orchestration techniques (prompt chaining, tool/function calling, structured outputs)
- Develop production-grade APIs and services (FastAPI/Flask/Streamlit) for GenAI applications
- Integrate LLM solutions with enterprise systems, data platforms, and workflows
- Apply guardrails and evaluation frameworks to improve response quality, reduce hallucinations, and ensure responsible AI usage
- Collaborate with Data Engineering and MLOps teams for data pipelines, deployment, monitoring, and scaling
- Contribute to reusable components, documentation, and engineering best practices
Experience & Core Requirements (Must-Have)
Overall Experience
- 6–9 years total experience
- 1–3+ years in hands-on GenAI / LLM application development (production use cases)
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
- Experience with fine-tuning techniques (LoRA, PEFT) or prompt tuning strategies
- Experience with enterprise GenAI security & privacy practices (data masking, access control, compliance)
- Familiarity with Azure AI ecosystem (Azure OpenAI, Azure AI Search, Fabric, etc.)
Exposure to agentic coding tools (e.g., Claude Code or similar environments)
ResponsibilitiesKey Responsibilities
- Design and develop LLM-powered applications using agentic patterns (single/multi-agent) for business use cases
- Build and optimise end-to-end RAG pipelines (ingestion, embeddings, retrieval, orchestration, response synthesis)
- Implement prompt engineering and orchestration techniques (prompt chaining, tool/function calling, structured outputs)
- Develop production-grade APIs and services (FastAPI/Flask/Streamlit) for GenAI applications
- Integrate LLM solutions with enterprise systems, data platforms, and workflows
- Apply guardrails and evaluation frameworks to improve response quality, reduce hallucinations, and ensure responsible AI usage
- Collaborate with Data Engineering and MLOps teams for data pipelines, deployment, monitoring, and scaling
- Contribute to reusable components, documentation, and engineering best practices
Experience & Core Requirements (Must-Have)
Overall Experience
- 6–9 years total experience
- 1–3+ years in hands-on GenAI / LLM application development (production use cases)
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
- Experience with fine-tuning techniques (LoRA, PEFT) or prompt tuning strategies
- Experience with enterprise GenAI security & privacy practices (data masking, access control, compliance)
- Familiarity with Azure AI ecosystem (Azure OpenAI, Azure AI Search, Fabric, etc.)
Exposure to agentic coding tools (e.g., Claude Code or similar environments)
QualificationsKey Responsibilities
- Design and develop LLM-powered applications using agentic patterns (single/multi-agent) for business use cases
- Build and optimise end-to-end RAG pipelines (ingestion, embeddings, retrieval, orchestration, response synthesis)
- Implement prompt engineering and orchestration techniques (prompt chaining, tool/function calling, structured outputs)
- Develop production-grade APIs and services (FastAPI/Flask/Streamlit) for GenAI applications
- Integrate LLM solutions with enterprise systems, data platforms, and workflows
- Apply guardrails and evaluation frameworks to improve response quality, reduce hallucinations, and ensure responsible AI usage
- Collaborate with Data Engineering and MLOps teams for data pipelines, deployment, monitoring, and scaling
- Contribute to reusable components, documentation, and engineering best practices
Experience & Core Requirements (Must-Have)
Overall Experience
- 6–9 years total experience
- 1–3+ years in hands-on GenAI / LLM application development (production use cases)
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
- Experience with fine-tuning techniques (LoRA, PEFT) or prompt tuning strategies
- Experience with enterprise GenAI security & privacy practices (data masking, access control, compliance)
- Familiarity with Azure AI ecosystem (Azure OpenAI, Azure AI Search, Fabric, etc.)
Exposure to agentic coding tools (e.g., Claude Code or similar environments)
EXL Chennai, Tamil Nadu, IND Office
Chennai, India
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What you need to know about the Chennai Tech Scene
To locals, it's no secret that South India is leading the charge in big data infrastructure. While the environmental impact of data centers has long been a concern, emerging hubs like Chennai are favored by companies seeking ready access to renewable energy resources, which provide more sustainable and cost-effective solutions. As a result, Chennai, along with neighboring Bengaluru and Hyderabad, is poised for significant growth, with a projected 65 percent increase in data center capacity over the next decade.
