DE&A - AIML - Deep Learning NLP
Key Responsibilities
AI Strategy & Leadership
- Define and lead the organisation’s AI strategy aligned with business objectives
- Identify opportunities where AI can drive measurable value and competitive advantage
- Establish AI governance, best practices, and delivery frameworks
- Act as a thought leader for AI across the organisation
End-to-End Delivery
- Lead AI initiatives from ideation, use case definition, and business case development through to production deployment
- Oversee solution design, model development, validation, and operationalisation (MLOps/AIOps)
- Ensure scalability, robustness, and ethical use of AI solutions
Business Partnership & Use Case Design
- Partner with business stakeholders to identify, prioritise, and refine AI use cases
- Translate business problems into AI/ML solutions and technical requirements
- Facilitate workshops and discovery sessions to co-create solutions
Responsibilities
Technical Expertise
- Maintain a strong understanding of AI/ML technologies (e.g., NLP, generative AI, predictive modelling)
- Guide architectural decisions for AI platforms, data pipelines, and integration
- Stay up to date with emerging AI trends and assess their applicability
AI Platform & Adoption
- Define and implement an AI platform strategy (tools, infrastructure, governance)
- Drive adoption of AI capabilities across teams and functions
- Establish reusable components, frameworks, and best practices
Training & Change Management
- Develop and deliver AI training programs for both technical and non-technical audiences
- Build AI literacy across the organisation
- Champion a data-driven and AI-enabled culture
Stakeholder Management
- Engage senior leadership and communicate AI vision, progress, and value
- Collaborate with data, engineering, product, and business teams
- Manage external partners and vendors where required
Qualifications
Required Skills & Experience
- Proven experience delivering AI/ML strategy from concept to production
- Strong understanding of AI/ML techniques, tools, and architectures
- Experience designing and implementing AI platforms and AIOps practices
- Demonstrated ability to translate business problems into AI solutions
- Experience working with cross-functional stakeholders at all levels
- Strong leadership and communication skills
Preferred Qualifications
- Background in Data Science, Machine Learning, Computer Science, or related field
- Experience with cloud platforms (e.g., AWS, Azure, GCP) and AI services
- Familiarity with generative AI and large language models
- Experience in driving organisational change and technology adoption
Key Success Metrics
- Successful delivery of AI use cases into production
- Measurable business impact from AI initiatives
- Adoption and utilisation of AI platforms across the organisation
- Increased AI literacy and capability within teams