AI Delivery Manager leading AI-powered and data-driven solutions at Convatec. Overseeing team and project execution with budgets up to $1.2 million.
Responsibilities
Define and drive Convatec’s AI strategy in alignment with enterprise transformation goals, ensuring AI initiatives deliver measurable business value.
Lead the end-to-end design, development, and deployment of AI-powered and data-driven solutions using Microsoft Azure AI services, Microsoft Fabric, and modern DevOps/MLOps practices.
Directly manage a team of Engineers, providing technical guidance, mentoring, and performance oversight.
Lead and coordinate workstreams involving over ten external consultants, including UX Lead, Power BI Lead, Fabric Architect, Enterprise Architect, cross-functional programme leads, and Data Analysts.
Own the execution and delivery of AI and data projects with budgets up to $1.2 million, including planning, resource allocation, and stakeholder communication.
Establish and enforce best practices in AI engineering, data architecture, and MLOps to ensure scalability, reliability, and compliance across all AI systems.
Work closely with Convatec delivery teams and business units to ensure AI solutions are integrated effectively and aligned with operational needs.
Make independent decisions on technical architecture, tooling, and delivery approaches within approved programme scopes.
Approve resource allocation and task prioritisation within the AI CoE team and associated external consultants.
Recommend and implement process improvements and technical standards without requiring prior approval.
Authorised to approve technical documentation, solution designs, and internal project deliverables.
Requirements
5+ years’ experience in AI, ML, or Data Engineering, including at least 2 years in a technical leadership capacity.
Strong expertise in Azure AI and ML services (Azure Machine Learning, Cognitive Services, Azure OpenAI).
Proficiency with Microsoft Fabric, Azure Data Factory, and Synapse for large-scale data integration.
Solid understanding of data engineering principles — data lakes, Lakehouse (Delta/Parquet), ETL/ELT design, and governance.
Strong skills in Python, SQL, and API development (REST, GraphQL).
Experience implementing DevOps/MLOps pipelines for deployment, testing, and monitoring.
Proven ability to lead and mentor teams, set technical standards, and ensure delivery quality.
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