Applied AI Manager

KEY RESPONSIBILITIES: 

Applied AI Delivery & Value 

• Own the end-to-end delivery of applied AI use cases across priority domains (e.g. pricing, net revenue management, marketing personalisation, operational efficiency, telemetry). 

• Ensure AI solutions move beyond proof-of-concept into production and sustained adoption. 

• Define success metrics and track business value, performance, and ROI. 

AI Solution Architecture & Patterns 

• Define and govern enterprise-level AI solution patterns (e.g. embedded AI, RAG, agent-based approaches). 

• Ensure solutions align with the broader enterprise architecture, security, and integration standards. 

• Act as a design authority for applied AI implementations rather than a hands-on platform builder. 

AI Strategy & Roadmap 

• Support, Shape and maintain the applied AI roadmap aligned to TMICC’s strategic objectives. 

• Prioritise use cases based on value, feasibility, and risk. 

• Support leadership decision-making with clear trade-offs and recommendations. 

Governance & Responsible AI 

• Ensure AI solutions comply with responsible AI principles, data privacy, and relevant regulatory requirements. 

• Embed governance into delivery processes rather than treating it as a separate control layer. 

• Partner closely with risk, security, data, and legal stakeholders. 

Collaboration & Orchestration 

• Work with domain SMEs, product owners, data scientists, engineers, and external partners to deliver scalable solutions. 

• Influence without direct authority across a federated delivery model. 

• Act as a trusted advisor to senior stakeholders on applied AI opportunities and limitations. 

 
WHAT THIS ROLE IS / IS NOT 

 
This role is: 

• Business-outcome driven 

• Applied and delivery-focused 

• Enterprise-wide in scope 

• Accountable for value, not just technology 

This role is not: 

• A pure data science or research role 

• A central AI platform engineering role 

• A role with direct line management of large delivery teams 

Innovation & Continuous Improvement 

• Monitor emerging AI capabilities and assess their practical relevance to TMICC. 

• Drive experimentation where appropriate, with a clear path to production or retirement. 

• Capture and share learnings across the organisation. 

SKILLS AND EXPERIENCE REQUIRED 

Experience 

• 4+ years’ experience in applied AI, advanced analytics, or technology delivery roles with strong business engagement. 

• Proven experience operationalising technology/ AI solutions in complex enterprise environments. 

• Experience working across multiple business domains, ideally within FMCG or similarly complex industries. 

Education: Degree in Science, Business, Computer Science, AI/ML, Information Technology, or a related field.  

Technical & Domain Knowledge 

• Strong understanding of applied AI approaches, including: 

• Machine learning fundamentals 

• Generative AI (LLMs, RAG, prompt-based and agentic patterns) 

• Experience delivering technology/ AI within modern enterprise technology landscapes (cloud, SaaS, data platforms). 

• Familiarity with AI lifecycle practices (e.g. MLOps / LLMOps concepts) and modern delivery models. 

• Awareness of responsible AI, data privacy, and AI risk management principles. 

Depth across all areas is not expected; strength in applied delivery and architectural judgement is essential. 

Soft Skills 

• Strong self-leadership and comfort operating in ambiguity. 

• Excellent stakeholder management and influencing skills. 

• Ability to translate complex technical concepts into clear business value narratives. 

• Pragmatic, outcome-oriented mindset with high ownership. 

LEADERSHIP BEHAVIOURS 

• Drives outcomes with accountability and pace. 

• Balances innovation with operational discipline. 

• Demonstrates strong business acumen and consumer awareness. 

• Builds trust through clarity, consistency, and delivery.