AI Knowledge Systems Specialist

Job Description The AI Knowledge Systems Specialist will design, build, and support enterprise AI-powered knowledge solutions that improve access to information, streamline workflows, and enhance organizational decision-making. This role will lead the development of structured knowledge repositories and AI-enabled workflows using approved tools, starting with a grant-writing knowledge repository. 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.

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https://insightglobal.com/workforce-privacy-policy/. Skills and Requirements 3+ years of experience across data engineering, knowledge management, informatics, workflow automation, business analysis, or related technical roles, with a proven ability to translate business or scientific requirements into practical solutions Hands-on experience leveraging LLM-based tools or enterprise AI platforms to support operational, research, or knowledge management workflows, including prompt design and reusable workflow patterns Strong experience organizing structured and unstructured data to enable search, retrieval, analysis, reporting, and improved AI-driven output quality Experience designing and supporting AI-assisted workflows (e.g., LLMs, RAG, enterprise search) with a focus on evaluating output quality, source accuracy, and user feedback Strong experience building knowledge structures, including taxonomies, metadata models, and reusable repositories, along with clear technical documentation and maintenance processes Familiarity with regulated or research environments (e.g., clinical trials, GxP, CDISC standards) and experience using Python, SQL, or similar tools for data preparation and workflow automation