AI Engineer

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.
• You will be the bridge between business teams and AI solutions. That means sitting with non-technical stakeholders, asking the right questions, mapping their workflows, and translating messy real-world problems into clear requirements before a single line of code is written. You're as much a translator and trusted partner to the business as you are an engineer.

Your profile

• 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.

• 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.
• We're looking for someone with hands-on experience building and shipping AI solutions in a professional setting. You'll feel at home taking ownership of your work from start to finish and making an impact within your first few weeks