AI Delivery Manager
SKILLS
FULL DESCRIPTION
The AI Delivery Manager plays a pivotal role within [Employer hidden — view at passion-project.co.uk]’s AI Centre of Excellence (CoE), serving as both a strategic and technical leader in the design, development, and deployment of AI-powered and data-driven solutions. This senior-level position is responsible for shaping and executing [Employer hidden]’s enterprise AI strategy, leveraging Microsoft Azure AI services, Microsoft Fabric, and modern DevOps and MLOps practices to accelerate transformation across the business.
Position overview
The AI Delivery Manager plays a pivotal role within [Employer hidden]’s AI Centre of Excellence (CoE), serving as both a strategic and technical leader in the design, development, and deployment of AI-powered and data-driven solutions. This senior-level position is responsible for shaping and executing [Employer hidden]’s enterprise AI strategy, leveraging Microsoft Azure AI services, Microsoft Fabric, and modern DevOps and MLOps practices to accelerate transformation across the business.
Key responsibilities and authority
- Strategic Leadership: Define and drive [Employer hidden]’s AI strategy in alignment with enterprise transformation goals, ensuring AI initiatives deliver measurable business value.
- Solution Delivery: 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.
- Team Management: Directly manage a team of Engineers, providing technical guidance, mentoring, and performance oversight.
- External Engagement: 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.
- Project Ownership: Own the execution and delivery of AI and data projects with budgets up to $1.2 million, including planning, resource allocation, and stakeholder communication.
- Technical Governance: Establish and enforce best practices in AI engineering, data architecture, and MLOps to ensure scalability, reliability, and compliance across all AI systems.
- Cross-functional Collaboration: Work closely with [Employer hidden] delivery teams and business units to ensure AI solutions are integrated effectively and aligned with operational needs.
- Decision-Making Authority: 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.
- Signatory Rights: Authorised to approve technical documentation, solution designs, and internal project deliverables.
Key requirements
Essential
- 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, Synapse, and Foundry 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.
Desirable
- Familiarity with Power BI, Dataverse, or Fabric + Power Platform integration.
- Experience with containerization (Docker/Kubernetes) and cloud automation.
- Exposure to agent-based AI architectures, prompt engineering, or retrieval-augmented generation (RAG).
- Awareness of responsible AI, data privacy, and security compliance frameworks.
Soft Skills & Attributes
- Hands-on leader with strong technical judgment and mentoring ability.
- Effective communicator who can translate complex AI concepts into business value.
- Proactive, collaborative, and comfortable working across disciplines and time zones.
Education/ Qualifications
- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
- Relevant certifications in Azure AI, Data Engineering, or MLOps are highly desirable.
Languages
- Speaking: Yes English*
- Writing/Reading: Yes English*
*additional languages as required or nice to have
Working conditions
Hybrid working 1 number of days per week in the office driven by business requirements as [Employer hidden] has a flexible approach to office working**.