Summary We are seeking a highly skilled and forward:thinking GenAI Engineer to join our AI innovation team. This role is ideal for someone with deep technical expertise in Generative AI, a strong foundation in Python programming, and a passion for driving enterprise AI transformation. You will be instrumental in designing, developing, and deploying advanced Retrieval:Augmented Generation (RAG) systems. You'll also play a pivotal role in enabling our internal workforce to embrace and adopt AI technologies. Your role in our mission typedisc: : Architect and implement scalable RAG systems using Python and modern GenAI tools. : Build custom pipelines for document ingestion, chunking strategies, and embedding generation. Working knowledge in LlamaIndex is preferable. : Have a deep knowledge in using AI augmented tools like GitHub Copilot. Experience in developing custom extensions : Evaluate and implement different embedding models (OpenAI, Azure OpenAI, Cohere, etc.) and chunking strategies (fixed:size, semantic:aware, overlap:based). : Create and optimize indexing strategies (vector, hybrid, keyword:based, hierarchical) for performance and accuracy. : Work with Azure AI Services, particularly Azure Cognitive Search and OpenAI integration, to deploy end:to:end AI applications. : Collaborate closely with cross:functional teams including data engineers, product managers, and domain experts. : Conduct AI enablement sessions, workshops, and hands:on labs to upskill internal teams on GenAI usage and best practices. : Participate in code reviews, contribute to best practices, and ensure the reliability, scalability, and maintainability of AI systems. What were looking for typedisc: : 5+ years of experience in software engineering, with strong expertise in Python. : Proven track record of building and deploying RAG:based GenAI solutions. : Hands:on experience with LlamaIndex, LangChain, or equivalent frameworks. : Familiarity with prompt engineering, prompt tuning, and managing custom Copilot extensions. : Strong understanding of LLMs, vector databases (like FAISS, Pinecone, Azure Cognitive Search), and embedding techniques. : Solid knowledge of Azure AI, cloud deployment, and enterprise integration strategies. : Proficiency with version control and collaborative development using GitHub. What you should expect in this role typedisc: : Architect and implement scalable RAG systems using Python and modern GenAI tools. : Build custom pipelines for document ingestion, chunking strategies, and embedding generation. Working knowledge in LlamaIndex is preferable. : Have a deep knowledge in using AI augmented tools like GitHub Copilot. Experience in developing custom extensions : Evaluate and implement different embedding models (OpenAI, Azure OpenAI, Cohere, etc.) and chunking strategies (fixed:size, semantic:aware, overlap:based). : Create and optimize indexing strategies (vector, hybrid, keyword:based, hierarchical) for performance and accuracy. : Work with Azure AI Services, particularly Azure Cognitive Search and OpenAI integration, to deploy end:to:end AI applications. : Collaborate closely with cross:functional teams including data engineers, product managers, and domain experts. : Conduct AI enablement sessions, workshops, and hands:on labs to upskill internal teams on GenAI usage and best practices. : Participate in code reviews, contribute to best practices, and ensure the reliability, scalability, and maintainability of AI systems.