Cloud Data Engineer

4w1 month ago

Hexaware

IN · Full-time · INR 2,000,000 – INR 3,500,000

About this role

This role focuses on building LLM-based agents for profiling, modeling, quality, and transformation tasks within a modern cloud and AI stack. You will collaborate with architects to ensure scalability and governance across data initiatives.

Day-to-day work involves developing sophisticated AI agents using LangGraph for complex data tasks like automated profiling, SQL generation, and data quality remediation. You will build and maintain LLM chains and workflows, ensuring high modularity and reusability while implementing RAG patterns.

You will collaborate with data science, engineering, and business teams to translate use cases into scalable AI solutions. The environment emphasizes hands-on work with Generative AI models, prompt engineering, and the Azure or AWS AI portfolio.

This position offers the opportunity to work on cutting-edge agentic orchestration and AI safety, including prompt versioning, guardrails, and evaluating LLM outputs. You will implement frameworks to monitor agent accuracy, latency, and cost using tools like RAG-evaluation metrics.

Requirements

Strong hands-on experience in Python for data processing, automation, and backend development
Experience working with Generative AI models and frameworks (LLMs, prompt engineering, RAG pipelines, model integration)
Hands-on expertise in LangChain and LangGraph for building stateful, multi-step agentic workflows
Proficiency in the Azure or AWS AI portfolio
Solid understanding of data pipeline orchestration
Knowledge of prompt versioning, guardrails, and evaluating LLM outputs for hallucination

Responsibilities

Build LLM-based agents for profiling, modeling, quality, and transformation tasks
Develop sophisticated AI agents using LangGraph for automated profiling, SQL generation, and data quality remediation
Build and maintain LLM chains and workflows, ensuring high modularity and reusability
Implement RAG (Retrieval-Augmented Generation) patterns
Collaborate with data science, engineering, and business teams to translate use cases into scalable AI solutions
Implement frameworks to monitor agent accuracy, latency, and cost using tools like RAG-evaluation metrics