AI Enablement Engineer

AI Enablement Engineer at Formula E – a role that bridges raw data systems and user-facing requirements to empower internal teams with cutting-edge AI tools in a fast-paced, safety-conscious environment.

Overview: The AI Enablement Engineer will be a primary architect that connects data platforms to business needs, ensuring secure, scalable, and efficient AI tooling across the organization.

Responsibilities:

• Custom Agent & App Engineering: Own the application lifecycle. Collaborate with cross-functional teams to map business logic, prototype solutions, and build functional web applications, software agents, and automated workflows using Python. Develop RAG pipelines and integrate AI environments with internal databases to transform workflows.
• Secure Sandboxing & GCP Compliance: Maintain secure Google Cloud Platform (GCP) sandbox environments, manage IAM permissions for least-privilege access, and ensure tools and AI deployments meet cybersecurity and data compliance policies, are cost-aware, and securely deployed.
• Productionising & Technical Handover: Turn early ideas into production-grade architectures. Work with Data and AI teams, PMs and BAs to ensure clean handover, well-organized code repositories, API schemas, and documentation for scalable AI ecosystems.

Qualifications:

• The Python & Cloud Craftsman: 2–3+ years of hands-on software development with production-grade Python, web applications, and API configuration in public cloud infrastructure, specifically Google Cloud Platform (GCP) including Vertex AI, Cloud Functions, and BigQuery.
• The Generative AI Architect: Strong understanding of modern LLM frameworks, vector databases (e.g., Pinecone, BigQuery Vector Search), and RAG pipeline architectures; experience integrating AI models into user-facing tools to solve real-world problems.
• The Technical Translator: Ability to translate manual workflows and non-technical needs into structured technical requirements and working code; strong collaboration and stakeholder engagement across model selection and application design.
• The Detail & Safety Oriented Professional: Self-starter with a focus on turning ideas into automated applications while upholding security, data privacy, and cost guardrails in a global environment.