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 or Master's degree in Computer Science, Engineering, or related field 3-8+ years of experience in software e...