What You'll Be Doing
As a Lead AI Engineer, you're the one steering the most complex, highÃÂâÃÂÃÂÃÂÃÂimpact work. You'll still build things hands-on, but you'll also be the person others come to for architectural decisions, delivery strategy, and coaching.
You'll get to:
Own one or more AI domains end-to-end (e.g., personalisation, optimisation, digital coaching - whatever your sweet spot is).
Set the technical direction for your space: architecture, modelling approach, evaluation strategy, reliability expectations.
Build and ship AI systems from idea all the way to production - and ensure they stay healthy once they're there.
Lead discovery, experiments, A/B tests, and scoping sessions, turning fuzzy business questions into a structured, prioritised roadmap.
Work with digital, data, and commercial teams across regions to align AI efforts and make smart trade-offs.
Build reusable templates, pipelines, APIs, and patterns that others can build on.
Coach other engineers, guide modelling decisions, and review work to keep quality consistently high.
Establish ways of working for your domain and influence how AI engineering operates across the organisation.
Become a trusted technical counterpart for senior stakeholders.
Champion safe, ethical, privacy-aware AI practices.
If you love solving hard problems, influencing strategy, mentoring others, and still getting stuck into code, this role was basically designed for you.
Who You Are
You don't have to tick every box - but if you're operating at a senior/lead level in ML, data science, or software engineering, and you care deeply about building real AI products, you're likely a great fit.
You'll thrive if:
You've spent around 10+ years building and shipping AI/ML systems (not just prototypes).
You've led delivery of complex AI initiatives, including coordinating other engineers or partners.
You've shaped an AI/ML roadmap before, or played a major role in one.
Python + SQL are your everyday tools, and you write production-grade code.
You're comfortable making system-level decisions: architecture, latency, observability, fallbacks, governance.
You have experience with ML frameworks (scikit-learn, XGBoost, PyTorch, TensorFlow, LightGBM).
You know your way around LLMs, RAG, prompt design, tuning, and evaluation.
You can review pipelines, data work, and MLOps architectures with confidence.
You're fluent in cloud platforms (Azure, AWS, or GCP all welcome).
You handle ambiguity well and can shape direction for yourself and others.
You communicate clearly and calmly with nonÃÂâÃÂÃÂÃÂÃÂtechnical audiences.
You actively mentor and love raising team standards.
You care about responsible AI, security, privacy, and long-term sustainability.
You're comfortable pushing back when an idea isn't viable - and suggesting a better path.