MLOps Engineer for Semantic Data Platform

Summary

* We are looking for a Senior MLOps/DevOps Engineer with Data Architecture, AI/ML skills, who will work on developing an AI platform with federated learning, cross-organization, and cross-domain collaboration. 
* Early-stage European startup
* Full-time long-term engagement.

Project Description

We are in the early stages of building a next-generation AI platform for creating, orchestrating, and durably executing data and ML workflows across heterogeneous data environments, streamlining cross-organization and cross-domain collaboration. 

The platform is a tool for data engineers and data scientists who deal with ontology, data extraction, workflow orchestration, and execute pipelines with multiple sources.

Some of the featured functions:

• Source connection and data interpretation
• Automated ontology and knowledge graph creation
• Self-healing infrastructure with semantic understanding
• Custom data pipeline building capabilities
• Bridge semantic and structural data gaps, and advanced ontology extraction.

Primary Responsibilities Include

• Infrastructure architecture: Platform from scratch, including integrating contextualization and schema mapping models into real-time workflows, metadata management, and end-to-end integration of system components.
• Collaborate with stakeholders (manage AI/ML complexities and contribute to system design)
• AWS Cloud Infrastructure
• Pipelines: Set up and optimize CI/CD pipelines (Python, Terraform, Kubernetes, Airflow) for custom ML and data workflows. 
• Deployment and monitoring Models: Deploy proprietary and third-party ML models, logging, performance reliability, and error detection. 
• Reliability and Automation: Automate infrastructure provisioning, testing, and deployment with Terraform, embedding fault-tolerance and observability to minimize downtime in complex and multi-source environments.
• Security & Compliance: Implement zero-trust security (e.g., attribute-based/role-based access control, encryption, and SSO)
• Collaboration & Mentorship: Work together with the lead AI engineer and other stakeholders in order to co-design orchestration tools and contribute to system design.

DevOps/MLOps Role Requirements

• Lead infrastructure scaling efforts as first/early DevOps hire
• AWS cloud
• GitHub Actions (or similar CI/CD), Kubernetes, MLOps (model deployment, versioning, monitoring)
• Python proficiency essential, GoLang beneficial
• Database knowledge: Postgres, Redis (required), Neo4j (nice-to-have)
• Monitoring tools: CloudWatch, Prometheus, Grafana, OpenTelemetry, Jaeger
• Infrastructure-as-Code experience (e.g., Terraform/OpenTofu)

General Team Skills Coverage:

1. Ontologies, Semantics, Knowledge Graphs
2. Data contextualization & transformation
3. Data federation & correlation
4. Time-series data & streaming data handling
5. RPA - Robotic Process Automation
6. MLOps
7. Policy engines
8. Backend (Go, Python)
9. Databases 
10. Infrastructure and DevOps (AWS)
11. Security & Zero Trust (attribute-based access control, role-based access control, encryption, SSO)
12. API & codeless integration agents (Zapier-like functionality)