AI Data Engineer - Manager

Deloitte Deloitte · Consulting

AI Data Engineer - Manager role focused on leading data architecture and engineering for AI/ML/GenAI solutions. Designs and operationalizes data foundations for LLM-powered applications, including RAG and vector search. Manages delivery teams, partners with stakeholders, and ensures data governance, quality, and monitoring. Blends technical leadership with delivery management and team development, driving engineering standards and outcomes in client environments. Contributes to AI/ML/GenAI technical direction, architectural design, technology selection, and MLOps/LLMOps. Conducts research for scalable AI solutions, leads development of AI models, and collaborates with various teams. Serves as a technical advisor, ensures operational excellence, and addresses risks and ethical considerations in AI implementation.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. This role blends hands-on technical leadership with delivery management and team development, driving consistent engineering standards and measurable outcomes in client environments.
  5. Help define the AI/ML/GenAI technical direction and vision, ensuring alignment with strategic goals and digital transformation efforts.

Skills

Required

  • Data architecture and engineering
  • AI/ML/GenAI solutions delivery
  • LLM application support (RAG, embeddings, vector search)
  • Data governance, lineage, quality controls, and monitoring
  • MLOps and LLMOps
  • Technical leadership
  • Delivery management
  • Team development
  • Agile methodology
  • Cloud and on-premises system integration
  • Machine learning, natural language processing, computer vision
  • Risk management and ethical AI implementation
  • Security and risk mitigation

Nice to have

  • Open-source and commercial technology selection
  • Containerization
  • CI/CD pipelines

What the JD emphasized

  • AI/ML/GenAI solutions
  • LLM-powered applications
  • RAG, embeddings, vector search
  • data governance, lineage, quality controls, and monitoring
  • MLOps and LLMOps
  • AI/ML/GenAI technical direction and vision
  • AI models (e.g., machine learning, natural language processing, computer vision)
  • ethical AI implementation
  • training data poisoning, AI model theft, and adversarial samples

Other signals

  • AI/ML/GenAI solutions
  • LLM-powered applications
  • RAG, embeddings, vector search
  • data governance, lineage, quality controls, and monitoring
  • MLOps and LLMOps