Key Responsibilities

• Design, build, and deploy LLM-powered applications for real-world use cases (e.g., document intelligence, copilots, knowledge systems)
• Develop and optimize Retrieval-Augmented Generation (RAG) pipelines, including ingestion, chunking, embedding, retrieval, and reranking
• Architect and implement end-to-end AI systems, from backend services to user-facing interfaces
• Work with structured and unstructured data, ensuring data quality, governance, and efficient retrieval
• Implement evaluation frameworks to measure model performance, latency, and hallucination rates
• Collaborate with data engineers and ML engineers to productionize models and pipelines
• Deploy and maintain systems on cloud platforms (AWS, GCP, or Azure), ensuring scalability and reliability
• Stay up to date with the latest advancements in LLMs, agent frameworks, and AI tooling

Requirements

• Bachelor's ...