Technology Architect - AI/GenAI-Q2 FY 27

Job description

## **Job Description:** - We are looking for a highly skilled Generative AI Engineer Senior Technologist with strong hands on experience in Python Large Language Models RAG Agentic AI fine tuning and cloud deployment - The ideal candidate should have a deep understanding of LLM architectures at a systems level and the ability to design build deploy and evaluate end to end AI solutions for enterprise use cases - This role requires strong engineering capability problem solving skills and practical exposure to modern AI frameworks cloud native deployment and model engineering concepts ## **Key Responsibilities:** - Design and develop enterprise grade Generative AI solutions using Python and modern AI frameworks - Build AI applications using LLMs embeddings prompts function calling tool usage and agentic AI patterns - Implement Retrieval Augmented Generation RAG pipelines for enterprise knowledge retrieval and generation use cases - Design end to end AI solutions covering - o Data ingestion - o Data processing - o Retrieval - o Generation - o Evaluation - Work with Agentic AI frameworks such as CrewAI LangChain LangGraph LlamaIndex AutoGen and Langfuse - Apply prompt engineering strategies to improve LLM accuracy reliability and usability - Evaluate the suitability of fine tuning vs RAG based on business and technical requirements - Support LLM fine tuning workflows including instruction tuning and supervised fine tuning - Manage data preparation for fine tuning including train validation splits data quality checks and contamination prevention - Deploy AI workloads on cloud platforms such as Azure AWS or GCP - Containerize AI applications using Docker and work with basic Kubernetes concepts - Apply model engineering best practices for scalable reliable and production ready AI systems ## **Technical Requirements:** ## **Additional Responsibilities:** ## **Preferred Skills:** Technology->AI-Responsible AI->Responsible AI->explainable ai,Technology->AI-AI Engineering->LLMOps