AI Knowledge Data Engineer

Job Description A client of Insight Global is seeking an experienced expert AI Knowledge Data Engineer to design, implement, and scale state-of-the-art AI systems that combine large language models (LLMs), advanced retrieval techniques, cognitive memory architectures, including knowledge representation, and data fusion. This individual will orchestrate robust data pipelines, architect scalable training data solutions, and build the foundational knowledge bases that power next-generation AI agents. They will collaborate with cross-functional teams to ensure our systems efficiently retrieve, contextualize, and generate accurate information for diverse business applications. 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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• Proven experience designing and scaling data pipelines and training data workflows for LLMs or similar AI systems.
• Strong background in information retrieval systems, vector search technologies, and RAG frameworks (e.g., FAISS, Pinecone, Elasticsearch, Milvus).
• Proficiency in programming (Python) and machine learning libraries (TensorFlow, PyTorch).
• Experience with ontologies, knowledge graphs, and semantic technologies (RDF, OWL, SPARQL).
• Familiarity with distributed data processing and orchestration tools (e.g., Spark, Airflow, Kubeflow).
• Experience with LLM fine-tuning, prompt engineering, and RAG optimization.
• Familiarity with data-centric AI principles and training data quality assessment.
• Experience with cloud platforms and scalable storage solutions.
• Background in cognitive memory architectures or AI agent design.