Engineering Manager - Regional Hub - J13271

About the Client: We are representing a highly established, large-scale enterprise with a dedicated AI/Data Lab. They are moving beyond proof-of-concept AI and are aggressively embedding Machine Learning, Generative AI, and Agentic AI into their core business operations. They operate a complex, shared IT infrastructure and require robust engineering governance to scale their AI capabilities efficiently across multiple business units. The Role & Landscape: This is not a model-building role. You will partner with the Data Science team to take their models and orchestrate the engineering required to deploy, monitor, and scale them in production.

• Manage, coach, and scale a geographically distributed team of Machine Learning Engineers.
• Own the end-to-end MLE delivery pipeline, ensuring solutions are secure, scalable, and maintainable.
• Drive "Engineering Transformation" by establishing standardized deployment workflows, reusable components, and robust CI/CD and LLMOps practices.
• Act as the technical authority for deployment architectures involving GenAI and Agentic AI.
• Navigate complex cross-functional dependencies, working daily with Programme Managers, IT, Data Engineering, and external vendors. What We Are Looking For:
• A seasoned Software or ML Engineer with at least 2 years of formal people management experience (conducting performance reviews, hiring, coaching).
• Deep practical experience taking Machine Learning and Generative AI applications into production.
• Strong command of modern infrastructure and deployment tooling (MLOps, LLMOps, CI/CD, Containerization).
• Familiarity with cloud ecosystems (Azure/Databricks preferred) and Kubernetes.
• The maturity to establish engineering standards without becoming a bottleneck, balancing speed-to-market with production reliability. Why Apply To This Role: You are joining at a critical inflection point. The business has proven the value of AI; your mandate is to build the engine that scales it. This offers immense autonomy to define engineering standards, adopt GenAI-assisted development tools, and build out a regional team within a highly stable, resource-rich enterprise environment.