Design and maintain MLOps infrastructure and deployment pipelines. Deploy and manage machine learning models in production environments. Collaborate with AI engineers to improve deployment reliability and scalability.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Nice to Have
Job Description
AI Infrastructure Engineer (MLOps) – Production AI Systems
Fully Remote (EU) | Company based in Berlin, Germany
Contract: 6 months + potential extensions
AI | MLOps | Cloud Infrastructure | FinTech AI
About the Role
We’re looking for an AI Infrastructure Engineer (MLOps) to support the deployment and scaling of production-grade AI systems used in real-time financial and risk-analysis environments.
You’ll work closely with AI engineers, backend teams, and platform specialists to build the infrastructure powering machine learning pipelines, model deployment, and monitoring systems at scale.
This role is ideal for engineers who enjoy working at the intersection of:
- cloud infrastructure
- DevOps
- distributed systems
- and AI operations.
What You’ll Be Working On
- Production ML pipelines supporting fraud detection and risk scoring systems
- Infrastructure for real-time model inference and monitoring
- Scalable environments for training, deployment, and lifecycle management
- Cloud-native AI systems operating in high-availability environments
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Key Responsibilities
- Design and maintain MLOps infrastructure and deployment pipelines
- Deploy and manage machine learning models in production environments
- Build and optimise CI/CD workflows for AI systems
- Manage containerised services using Docker and Kubernetes
- Implement monitoring and observability for ML systems and infrastructure
- Collaborate with AI engineers to improve deployment reliability and scalability
- Support model versioning, rollback strategies, and lifecycle management
Required Skills & Experience
- 4+ years experience in DevOps, platform engineering, or MLOps
- Strong experience with Kubernetes and container orchestration
- Hands-on experience with Docker and cloud-native environments
- Experience with ML lifecycle tooling (MLflow, Airflow, Kubeflow, or similar)
- Experience with Infrastructure as Code (Terraform preferred)
- Familiarity with cloud platforms (AWS, GCP, or Azure)
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Nice to Have
- Experience supporting AI or data-heavy platforms
- Familiarity with real-time inference systems
- Exposure to FinTech, fraud detection, or high-volume transactional systems
- Experience with observability stacks and monitoring tools
Why Join?
- Work on large-scale AI infrastructure used in production
- Fully remote flexibility across Europe
- Long-term contract potential with extension opportunities
- High-impact role within Berlin’s growing AI and FinTech ecosystem
If you’re passionate about building scalable infrastructure for real-world AI systems, we’d love to hear from you.
Apply via XpertDirect — connecting companies with advanced AI and infrastructure specialists.
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