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You’ll be building AI that reaches a global audience, solves practical business problems, shapes digital experiences, lives in production, gives you room to experiment with modern AI tooling, and lets you contribute to templates, patterns, and ways of working that others will build on. The roadmap is full of interesting challenges in personalisation, optimisation, automation, and experience design.
Job Responsibility:
Build AI-powered products from the first whiteboard sketch all the way to live deployment
Blend software engineering, ML modelling, and data engineering to create genuinely useful tools
Work with teams across digital, marketing, operations, and customer experience to understand real problems and craft clear, measurable AI solutions
Handle everything from classical ML to LLM-powered systems (RAG, prompt design, adapters, etc.)
Integrate models into apps, websites, CRM platforms, internal tools
Keep things reliable with proper MLOps: monitoring, drift checks, retraining, alerting
Follow a thoughtful governance framework that keeps AI safe, private, fair, and transparent
Share patterns, build reusable components, and level up the organisation’s AI maturity
Requirements:
6+ years working with ML, data science, or software engineering in an applied, production-focused environment
Deployed real models
Python and SQL
Comfortable with ML frameworks (scikit-learn, LightGBM, PyTorch, TensorFlow etc.)
Built or used pipelines, containers, registries, tracking tools, or other MLOps tech
Familiar with cloud platforms (Azure, AWS or GCP)
Great at translating technical work into plain, friendly language
Like experimenting quickly but also care about building things that last
Stay curious about new AI developments but lean practical, not hype-driven
Understand the importance of data privacy and responsible AI