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AI Data Engineer Manager
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Deloitte
Kansas City, MO (In Person)
Full-Time
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Job Description
AI Data EngineerManager Position Summary Our Human Capital practice is at the forefront of transforming the nature of work. As converging forces reshape industries, our team uniquely addresses the complexities of work, workforce, and workplace dynamics. We leverage sector-specific insights and cross-domain perspectives to help organizations tackle their most challenging workforce issues and align talent strategies with their strategic visions. Our practice is renowned for making work better for humans and humans better at work. Be part of this exciting era of change and join us on this transformative journey. Recruiting for this role ends on 08/30/2026. Work you'll do The AI Data Engineer Manager will lead the data architecture and engineering delivery that enables AI/ML/GenAI solutions, ensuring data is trusted, secure, observable, and scalable from ingestion through consumption. You will design and operationalize modern data and retrieval foundations to support LLM -powered applications (e.g., Claude, GPT /Codex, Gemini) including patterns such as RAG , embeddings, vector search, and governed access to structured and unstructured data. You will manage day-to-day delivery with an onshore/offshore team, partnering with data science, ML engineering, and product stakeholders to translate use cases into production-ready pipelines and platforms with strong data governance, lineage, quality controls, and monitoring. This role blends hands-on technical leadership with delivery management and team development, driving consistent engineering standards and measurable outcomes in client environments. Strategic Alignment and Vision
- Help define the AI/ML/GenAI technical direction and vision, ensuring alignment with strategic goals and digital transformation efforts.
- Translate the vision of business leaders into realistic technical implementations, while identifying misaligned initiatives and impractical use cases Architectural Design
- Design end-to-end AI architectures, from data ingestion to model deployment, integrating with cloud and on-premises systems. Design and Technology Selection
- Select appropriate technologies from a pool of open-source and commercial offerings, considering deployment models and integration with existing tools.
- Understand and contribute to MLOps and LLMOps, focusing on operational capabilities and infrastructure to deploy and manage machine learning models and large language models. Research and Development
- Conduct research to pro To view full details and how to apply, please login or create a Job Seeker account