Gen AI Engineer – RAG Systems, AI Transformation

Job Description

Job Description:
• 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.
• 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.
• 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.

Requirements:
• 5+ years of experience in software engineering
• 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.

Benefits:
• Professional development opportunities