MLOps Engineer

Complexio’s Foundational AI platform automates business processes by ingesting and understanding complete enterprise data—both structured and unstructured. Through proprietary models, knowledge graphs, and orchestration layers, Complexio maps human-computer interactions and autonomously executes complex workflows at scale.

Established as a joint venture between Hafnia and Símbolo—with partners including Marfin Management, C Transport Maritime, BW Epic Kosan, and Trans Sea Transport—Complexio is redefining enterprise productivity through context-aware, privacy-first automation.

• Infrastructure Management: Architect and manage scalable cloud infrastructure workloads, including container orchestration and automated testing.
• Research Collaboration: Partner closely with data scientists and research teams to translate experimental models into robust, production-ready systems.
• DevOps Best Practices: Establish infrastructure as code, CI/CD pipelines, automated deployments, and comprehensive logging/monitoring.

Requirements

Qualifications

• 5+ years of experience after completing higher education.
• Advanced Python Programming: Production Python experience with web frameworks (FastAPI, Flask), testing frameworks,
• Cloud Computing Expertise: Hands-on experience with major cloud platforms (AWS, GCP, or Azure), including Kubernetes services (EKS/GKE/AKS).
• Research Team Collaboration: Experience working with data science or research teams, effectively translating experimental code into production systems.
• Software Engineering: Strong foundation in version control, testing strategies, software architecture principles, async programming, and concurrent system design.
• Data Infrastructure: Design and implement scalable data infrastructure solutions leveraging distributed computing frameworks like Apache Spark or similar for large-scale data processing. Build and optimize data lake architectures to support analytics, ensuring high performance, reliability, and data governance across large datasets.
• ML experience not required, but you should know why you want to work in this field.
• English min B2.

Nice to have

• ML libraries (PyTorch, scikit-learn, numpy).
• Production ML Pipeline Development: Design, build, and maintain end-to-end ML pipelines from data ingestion to model deployment and monitoring.
• ML Infrastructure: Experience with MLOps tools (MLflow, Kubeflow), container technologies (Docker, Kubernetes), inference engines (vLLM, SGLang), distributed computing (Ray.io), and data labeling platforms (Label Studio).
• Managed ML services (SageMaker, Vertex AI.

Benefits

• Join a pioneering joint venture at the intersection of AI and industry transformation.
• Work with a diverse and collaborative team of experts from various disciplines.
• Opportunity for professional growth and continuous learning in a dynamic field.