Engineer II (AI Enablement)

Location: Hybrid in Singapore, Paris, or Athens

Company

Engineering Cloud Platform work focused on enabling company-wide adoption of AI through internal tools, agents, assistants, and integrations.

What you will do

• Build, tailor, deploy, and operate AI agents and assistants for internal functions.
• Develop and maintain knowledge-base pipelines that collect, structure, and update source content.
• Integrate AI systems with company tools and data sources through secure APIs and connectors.
• Build user-facing interfaces and backend services for internal tooling.
• Apply access control, data privacy, human-in-the-loop safeguards, and responsible-AI practices.
• Monitor adoption, quality, cost, reliability, and observability while contributing to incident response, RCA, documentation, and shared engineering frameworks.

Requirements

• 3+ years of professional engineering experience.
• Experience building and operating production services, APIs, or tooling end-to-end.
• Hands-on experience building with LLMs, including model APIs, prompt and context engineering, or basic RAG patterns.
• Proficiency in Python and/or TypeScript, with knowledge of system design, databases, data modelling, and application architecture.
• Experience with API design and data integration across multiple systems, plus SQL and database design.
• Familiarity with cloud infrastructure, preferably AWS, as well as Docker, Kubernetes, and CI/CD pipelines.

Nice to have

• Experience with agent frameworks, MCP-style tool interfaces, RAG, or knowledge-base systems.
• Experience integrating third-party SaaS APIs.
• Full-stack experience building web UIs and supporting backend services.
• Experience building internal or developer-facing tools.
• Exposure to LLM evaluation, observability, or prompt and context engineering.

Culture & Benefits

• Work as a strong individual contributor with ownership across the full feature and project lifecycle.
• Collaborate with a senior engineer and a lead in a fast-moving, high-visibility AI enablement area.
• Gather feedback from internal users and turn friction points into product improvements.
• Contribute to AI-assisted engineering practices and shared frameworks.