Gen AI Engineer

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 Enable the workforce to adopt an AI first strategy by leveraging AI code assistance tools 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 we're looking for 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 Enable the workforce to adopt an AI first strategy by leveraging AI code assistance tools 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.