AI Media Backend Engineer (m/w/d) Python, Cloud Native & Docker/Kubernetes - remote

Für unseren Kunden suchen wir ab sofort einen AI Media Backend Engineer (m/w/d) Python, Cloud Native & Docker/Kubernetes für die voraussichtliche Dauer bis 12/2026 mit der Option auf Verlängerung. Der Einsatz findet remote statt.

Aufgaben:

- Develop and refine backend services, APIs and integration components for selected AI media-generation capabilities

- Integrate independent image, video and audio backends with the central Model Context Protocol (MCP) platform

- Implement scalable and robust request handling, orchestration and processing patterns for parallel media-generation workloads

- Support the implementation of backend capabilities for jointly prioritised initial use cases

- Contribute to containerisation, deployment automation, logging, error handling and technical documentation

- Identify and address backend-related performance, scalability and security considerations

- Work closely with the Lead AI Platform & MCP Architect and the Pipeline & Brand Compliance Engineer

Must Have:

- Experience integrating AI services, AI models or external AI APIs into enterprise applications

- Strong hands-on software-engineering experience in backend development for cloud-native applications

- Strong programming skills in Python; additional experience in TypeScript, Java, Go or similar backend technologies is beneficial

- Practical experience designing and implementing REST APIs, asynchronous processing, queues and service integrations

- Experience with container technologies, especially Docker and Kubernetes or equivalent managed container platforms

- Understanding of scalable backend patterns for compute-intensive or asynchronous workloads

- Knowledge of secure backend development, API security and enterprise integration practices

- Ability to work independently while collaborating closely in a cross-functional technical team

- Strong communication skills in English

Nice to have:

- Experience with MCP-/FastMCP-based services

- Experience with image, video, audio or multimodal AI services

- Experience with Azure services and CI/CD pipelines

- Experience with GPU-enabled workloads or AI inference infrastructure