Applied AI Engineer & AI Enablement

Mission

Build and deploy a wide range of AI solutions end-to-end: forecasting algorithms, classification and regression models, LLM-powered tools, AI agents, or any technique best suited to the problem at hand.

Own the full lifecycle of every solution: from design and implementation through production deployment, monitoring, and continuous improvement based on user feedback.

Collaborate closely with the AI Platform team to leverage shared infrastructure, align on technical architecture, and get guidance on the AI stack.

Communicate progress and findings clearly to both technical peers and non-technical stakeholders; present results to chapter leads and business owners.

Produce lightweight documentation and run enablement sessions so business teams can work confidently alongside the solutions you create.

Build from the ground up: Join us at the beginning of this journey, working closely with the team Lead to shape the strategy, culture, and technical foundations of our new team.

Your profile

Journey to impact:

Month 1: Get up to speed with 1–2 business units. Understand their data, workflows, and business context. Pick up the first strategic brief from the AI Strategist Principal, and deliver a working AI prototype that solves a real daily pain point.

Month 3: Have at least one solution running in production. Incorporate feedback from real users, stabilise monitoring, and document the solution. Begin a second chapter engagement and broaden the range of AI techniques you apply.

Month 6: Become a trusted enabler within the company and the Strategy team. Your solutions are running reliably and reusable components are being picked up by other teams.

We're looking for someone at the intersection of AI engineering and business understanding, who can turn a department's problems into working AI solutions.

AI & technical foundation: Strong Python skills and practical experience across the AI/ML spectrum: forecasting, classification, regression, LLMs, and agentic frameworks (LangChain, LlamaIndex, CrewAI, or equivalent). Comfortable owning solutions in production. Familiarity with Docker/K8s or MLOps tooling.

Engineering fundamentals: Working knowledge of cloud platforms (AWS, GCP, or Azure), REST APIs (FastAPI or similar), and SQL for data access and integration.

Business & requirements acumen: Able to sit with a non-technical team, ask the right questions, identify what actually matters, and translate messy real-world context into a clear implementation plan.

Communication & collaboration: Clear and confident communicator with both technical and non-technical audiences. You can explain what a model does, and what it can't do, to someone with no AI background.

Startup mindset & ownership: Comfortable with ambiguity, proactive, and accountable. You have the ability to take a vision and bring it to life.