Engineering Manager – AI & Data

About the Role The Engineering Manager – AI & Data is responsible for leading the design, development, and operation of enterprise AI, analytics, and data engineering platforms. The role ensures secure, scalable, and compliant delivery of AI, machine learning, and data solutions. Responsibilities • Lead end-to-end delivery of AI/ML and data platforms (architecture, engineering, operations, and continuous improvement) • Own data and AI reference architecture and engineering standards across Azure and Google Cloud (GCP) • Lead lakehouse and data platform delivery using Databricks, Delta Lake, and enterprise data lake patterns • Design and govern enterprise data pipelines: data ingestion, ETL/ELT, batch/stream processing, and orchestration • Define and enforce data modeling standards (dimensional, Data Vault, and medallion/layered approaches) to enable trusted analytics • Establish and run MLOps/LLMOps practices: model lifecycle management, CI/CD, monitoring, evaluation, and guardrails • Deliver LLM-enabled solutions, including RAG patterns, tool/function calling, agentic architectures, and data agents for automation • Oversee use of Azure AI Foundry and Google Vertex AI for model development, deployment, and governed experimentation • Enable enterprise BI and self-service analytics through Power BI, including semantic models, DAX standards, and governance • Establish enterprise semantic layer and ontology standards (business glossary/metrics definitions) to ensure consistent reporting and AI understanding • Ensure strong data governance, security, and PDPL compliance across platforms and products • Implement governed access, lineage, and data sharing using Unity Catalog (or equivalent) aligned with PDPL controls • Manage and coach data engineering and ML engineering teams; set objectives, build capability, and drive delivery excellence • Manage vendors, partners, and cloud providers; control cost, performance, and SLAs Required Skills • Databricks (Spark, Delta Lake, jobs/orchestration) • Data lake / lakehouse architecture and design • Databricks Unity Catalog (data governance, access control, lineage) • Ontology and semantic modeling (business glossary, knowledge graph concepts) • Azure AI Foundry (model management, prompt/agent workflows) • Google Vertex AI (training, deployment, pipelines) • LLMs and agentic architectures (RAG, tool/function calling, evaluation/guardrails) • Data agents and automation patterns • Power BI (semantic models, DAX, governance; semantic layer) • Cloud platforms: Microsoft Azure and Google Cloud (GCP) • Data engineering: data ingestion, ETL/ELT, batch/stream processing • Data modeling (dimensional, Data Vault, medallion patterns) Show more Show less