Design, experiment, and implement AI solutions (e.g., RAG chatbots, document processing, and data transformation) on the EDMS platform.

Develop and optimise Retrieval Augmented Generation (RAG) pipelines including chunking, embeddings, vector search, and prompt engineering.

Integrate AI models with EDMS data pipelines for structured data ingestion, cleansing, and database write-back.

Conduct model evaluation, monitor accuracy, and perform fine-tuning using feedback logs and performance metrics.

Collaborate with data engineers, product owners, and business users to translate use cases into scalable AI features.

Document AI solution designs, RAG workflows, data flows, and deployment considerations across development stages.

Requirements

• Hands‑on experience with Generative AI, Large Language Models (LLMs), and RAG‑based architectures.
• Proficiency in Python and AI/ML libraries for NLP, embeddings, and vector da...