Bupa is establishing an AI Factory that will provide the processes, products, capabilities, and governance to execute and support AI adoption and use cases at Bupa. This team will host, support, develop, and lifecycle manage internally facing tools and customer facing products utilising AI based technologies.
An AI Delivery Manager (AI DM) is responsible for overseeing the delivery of AI projects, ensuring that they are completed on time, within scope, and meet the desired quality standards. The role combines project management with a deep understanding of AI technologies, making it crucial for ensuring the successful implementation of AI solutions.
**Responsibilities**
**Business Outcomes**
- Lead an end-to-end delivery of technology-enabled products, initiatives and/or services.
- Ensure that the delivery team's purpose and objectives are clear and linked to the broader vision, strategy, and customer roadmap.
- Work closely with the Product Owner and Team, focus team on what is needed to deliver business outcomes that is the best value approach and in alignment with defined outcomes / objectives of the sprint and program increment.
**Agile Practices Governance and Management**
- Facilitate and run day to day agile ceremonies including stand up's, sprint planning, retros and showcases.
- Lead PI planning event for the team and co-ordinate external team dependencies to ensure visibility of upcoming delivery.
- Communication of the team delivery commitments and progress updates (both at initiative and overall team levels)
- Shield the team and assist in the removal of blockers and other delivery obstacles.
**Coach, Mentoring and Guiding**
- Facilitate team improvements and contribute to the Way of Working.
**Relationship Building and other management responsibilities.**
- Build strong relationships with internal customers, stakeholders, and integration teams to ensure delivery is smooth and customer centric
- Other responsibilities as directed by the Assignment or Reporting Manager
**Community**
- Proactively support the uplift of project delivery practices and participate in and contribute to Communities of Practice within Bupa
You may also carry out any other duties reasonably requested by Bupa from time to time.
**Qualifications, Skills, and Experience**:
**Project Management**
- ** Agile Methodologies**: Proficiency in Agile practices like Scrum, Kanban, or Lean, which are essential for managing AI projects that require iterative development.
- ** Scope Management**: Ability to define project scope clearly and manage changes effectively without derailing the project.
- ** Risk Management**: Identifying potential risks in AI projects (e.g., data issues, model accuracy) and developing mitigation strategies.
- ** Resource Management**: Efficiently allocating resources, including data scientists, engineers, and infrastructure, to ensure project success.
**AI and Machine Learning Knowledge**
- ** Understanding AI Concepts**: Familiarity with concepts like Large Language Models, Natural Language Processing and related concepts
- ** Technical Acumen**: Ability to understand technical details, challenges, and solutions related to AI projects, even if not directly involved in coding.
- ** Tooling and Products**: Knowledge of AI tools and products
**Communication and Collaboration**
- ** Stakeholder Management**: Engaging with stakeholders (clients, executives, team members) to understand their needs and keep them informed about project progress.
- ** Technical and Non-Technical Communication**: Ability to translate complex AI concepts into understandable terms for non-technical stakeholders.
- ** Team Collaboration**: Working closely with cross-functional teams, including data scientists, engineers, product owners, and developers
**Data Management**
- ** Data Governance**: Understanding of data governance principles, ensuring compliance with data privacy laws and policies.
- ** Data Quality Assurance**: Ensuring the quality of data used in AI projects is high, which is crucial for model accuracy.
- ** Data Integration**: Knowledge of data integration processes and challenges when dealing with multiple data sources.
**Problem-Solving and Decision-Making**
- ** Analytical Thinking**: Ability to analyze complex problems, identify root causes, and develop effective solutions in the context of AI projects.
- ** Decision-Making Under Uncertainty**: Making informed decisions in situations where data may be incomplete or uncertain, which is common in AI projects.
**Relationship Management**
- ** Engagement**: Building strong relationships with clients, understanding their business needs, and ensuring that AI solutions align with those needs.
- ** Expectation Management**: Setting realistic expectations with clients about the capabilities, timelines, and outcomes of AI projects.
**Continuous Learning and Adaptability**
- ** Staying Current**: Keeping up to date with the latest AI trends, tools, and meth