Job Description We are hiring two AI/LLM Engineers (1 Mid-Level, 1 Senior) to join a small, focused team building backend features for an existing Retrieval-Augmented Generation (RAG) service. You will be primarily coding in Python, creating API endpoints, developing LangGraph plugins that integrate with the content retrieval pipeline. You’ll work across the full RAG workflow—data ingestion, embeddings, retrieval, prompt engineering, generation, and evaluation—ensuring reliable production performance. Must be able to work independently, communicate clearly, and collaborate to refine workflows. This is a fast paced role that requires delivering high quality, well architected backend functionality for client facing solutions. We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/. Skills and Requirements • 5-7+ years of professional experience supporting RAG-based AI workflows • Strong Python skills, API creation, model training, testing and general backend programming • Experience building LangGraph plugins that integrate retrieval components with existing LLMs • Hands on experience in building end to end RAG pipelines, including prompt engineering, data ingestion, embedding, retrieval, generation and evaluation • Skilled in integrating APIs with LLMs • Working knowledge of agentic systems and retrieval algorithms • Strong communication skills with the ability to work independently while providing clear, consistent updates to the team. • Proficiency with GitHub and version control workflows • Experience with model and pipeline evaluation, including the use of RAG specific metrics (RAGAS: accuracy, relevance, and groundedness)