AI Engineer

* **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.  **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. * 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