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.

• 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.