Context Engineer

About the Role We are looking for a skilled Context Engineer to help build reliable, accurate, and domain-aware AI solutions across research, commercial, medical writing, and back-office processes. In this role, you will design and optimize the context layer that powers Large Language Models (LLMs) and AI agents, ensuring outputs are grounded in trusted knowledge sources, traceable, and aligned with business and regulatory requirements. Key Responsibilities Design and manage context engineering solutions, including prompt strategies, Retrieval-Augmented Generation (RAG) pipelines, grounding sources, and memory management. Integrate ontologies, semantic layers, and knowledge graphs to provide structured, relevant, and reliable context to LLMs and AI agents. Build and optimize agent instructions, tool definitions, and orchestration workflows using Azure AI Foundry . Develop and maintain evaluation frameworks to measure groundedness, relevance, hallucination rates, and medical and regulatory accuracy. Continuously improve context quality, retrieval performance, and AI-generated outputs based on evaluation results and user feedback. Collaborate with business stakeholders and subject matter experts across Medical Affairs, R&D, Commercial, and other functions to translate domain knowledge into effective AI solutions. Ensure AI-generated outputs are accurate, explainable, traceable, and compliant with applicable business and regulatory standards. Required Skills & Qualifications Experience in Generative AI, Large Language Models (LLMs), prompt engineering, and context engineering. Hands-on experience designing and implementing RAG pipelines and knowledge-grounding solutions. Experience with AI agent development, tool/function calling, and agent orchestration. Familiarity with Azure AI Foundry and Azure OpenAI services. Understanding of vector databases, embeddings, semantic search, and information retrieval. Experience working with knowledge graphs, ontologies, semantic layers, or structured knowledge systems. Knowledge of LLM evaluation techniques, hallucination mitigation, and responsible AI practices. Strong analytical, problem-solving, and stakeholder collaboration skills. Preferred Qualifications Experience in the pharmaceutical, healthcare, life sciences, or other regulated industries. Exposure to medical writing, clinical research, regulatory content, or competitive intelligence. Experience developing AI solutions that require high levels of factual accuracy, traceability, and governance.