About the Role :

We are looking for a highly skilled Data Engineer AI / LLM to join our growing AI and Data Engineering team.

The ideal candidate will be an Individual Contributor with strong expertise in Data Engineering, Generative AI, Large Language Models (LLMs), and Agentic AI frameworks.

This role involves understanding customer business challenges, identifying AI-driven opportunities, building Proof of Concepts (POCs), and transforming them into scalable, enterprise-grade production solutions.

Key Responsibilities:

- Understand customer business processes, data ecosystems, and technology landscapes to identify transformation opportunities.

- Identify, define, and design AI/LLM-driven use cases aligned with business objectives.

- Develop Proof of Concepts (POCs) to validate technical feasibility and business value.

- Convert successful POCs into scalable, secure, and production-ready AI solutions.

- Design and implement LLM-based applications using Retrieval-Augmented Generation (RAG), embeddings, vector databases, and prompt engineering techniques.

- Build and optimize Agentic AI workflows using frameworks such as LangChain, LlamaIndex, or similar technologies.

- Develop and manage scalable data pipelines using Python, SQL, Spark, and ETL/ELT frameworks.

- Collaborate with business stakeholders, architects, and engineering teams to ensure successful solution delivery.

- Integrate AI solutions with cloud platforms and enterprise systems.

- Ensure best practices in scalability, performance optimization, monitoring, and deployment automation.

- Stay updated with the latest advancements in Generative AI, Agentic AI, and Data Engineering technologies.

Required Skills:

- Strong Data Engineering experience (Python, SQL, Spark, ETL/ELT)

- Experience with cloud platforms (AWS / Azure / GCP)

- Hands-on with LLMs, RAG, vector databases, prompt engineering

- Experience with Agentic AI frameworks (LangChain, LlamaIndex, etc.)

- Ability to design scalable, production-grade systems

Preferred Qualifications:

- Experience deploying AI solutions in enterprise environments.

- Knowledge of MLOps, model deployment, and monitoring practices.

- Exposure to APIs, microservices, and containerization technologies.

- Experience with orchestration and workflow automation tools