Infrastructure & MLOps Engineer for AI Data Platform
C1 (Advanced) English
Senior (5-10 years)
Europe
UTC+01:00
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Summary
Join our team of top engineers and researchers building a next-generation AI platform for cross-industry knowledge exchange and collaboration.
You’ll design and scale cloud infrastructure for intelligent data workflows, federated learning, and large-scale ML pipelines across diverse domains.
What you’ll bring:
*Strong MLOps / DevOps experience (preferably in data-intensive environments)
*Track record of building and scaling AWS infrastructure for AI/ML workloads
*Strong expertise in infrastructure as code, CI/CD and observability
* Nice to have: experience in federated learning or ontology-driven systems infrastructure, scalability and security
* Long-term, full-time role with big influence, ownership and room to grow
Are you a talented developer looking for a remote job that lets you show your skills and get decent compensation? Join Upstaff.com, a platform that connects you with hand-picked startups and scale-ups in the US and Europe.
Required Skills
Nice to Have
The project
Join our team of researchers and engineers building a next-generation AI platform for semantic- and ontology-driven cross-organization and cross-domain data collaboration. The system is designed to be a game-changer for data engineers and scientists working with multiple knowledge domains and organizations. It enables AI-powered creation, orchestration, and durable execution of Data & ML workflows across heterogeneous data environments. By bridging semantic and structural gaps, the platform makes complex, multi-source data and ML workflows more reliable, interpretable, and scalable than ever, setting a new standard in knowledge collaboration.
Key features:
- Bridging semantic and structural gaps across organizations and industries
- Intelligent data & workflow environment with deep semantic understanding and federated learning
- AI-driven ontology and knowledge graph generation and management
- Automatic discovery, connection, and interpretation of multiple data sources
- Self-healing workflows for ML and data processing
Your role
As a Senior MLOps / DevOps Engineer, you will play a key role in making the system reliable, scalable, and production-ready. Your focus will be on designing and operating the AWS infrastructure that powers data and ML workflows across multiple domains. Day to day, you will:
- Architect, develop, and scale AWS-based infrastructure for AI/ML and data workflows
- Design and optimize CI/CD pipelines (Terraform, Jenkins, Kubernetes)
- Develop and monitor ML & data pipelines, ensuring performance and reliability
- Automate provisioning, testing, and deployment with Infrastructure-as-Code
- Integrate system components end-to-end (schema mapping, metadata, real-time workflows)
- Collaborate closely with AI researchers and engineers on integration, orchestration and improvements
- Ensure security, compliance, and observability in multi-source environments
What we’re looking for
- Strong background in AWS infrastructure for data/ML workloads
- Hands-on experience with CI/CD: Jenkins, GitHub Actions, Kubernetes, model deployment & monitoring
- Proficiency in Infrastructure-as-Code (Terraform / OpenTofu or similar tools)
- Databases: Postgres, Redis (MongoDB/Neo4j would be plus)
- Experience with monitoring & observability: CloudWatch or Prometheus or Grafana / OpenTelemetry /Jaeger or similar
- Infrastructure-as-Code (Terraform/OpenTofu)
- Languages, besides DevOps toolkit: Python. (Go would be a plus)
Tech we also touch
- Ontologies, Knowledge Graphs, Semantic data
- Custom & common ML models, contextualization, federation
- Time-series & streaming data
- Security (Zero Trust, ABAC/RBAC, IAM, SSO, encryption)
- Codeless integration agents & APIs
Why join us?
- Work on an ambitious AI project designed to transform how organizations collaborate on data and ML
- Collaborate with top engineers and researchers in a highly technical, innovation-driven environment
- Build infrastructure that bridges industries and domains — your work has direct, real-world impact
- Full ownership: from architecture to automation, you’ll define the overall approach and design the majority of critical components