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.
We are seeking a versatile MLOps Engineer to bridge the gap between data science research and production-ready machine learning systems. This role requires a complete engineering skillset spanning Python development, cloud infrastructure, and collaborative work with research teams.
We're looking for a complete engineer who can seamlessly transition between writing production Python code, designing cloud architectures, and collaborating with researchers on cutting-edge ML projects. You should be equally comfortable debugging a Kubernetes deployment, optimising a training pipeline, and explaining technical trade-offs to data scientists.
Some of the Responsibilities include
- Production ML Pipeline Development: Design, build, and maintain end-to-end ML pipelines from data ingestion to model deployment and monitoring
- Infrastructure Management: Architect and manage scalable cloud infrastructure for ML 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
- Advanced Python Programming: Production Python experience with web frameworks (FastAPI, Flask), testing frameworks, and ML libraries (PyTorch, scikit-learn, numpy) a great-to-have
- Cloud Computing Expertise: Hands-on experience with major cloud platforms (AWS, GCP, or Azure), including Kubernetes services (EKS/GKE/AKS) and managed ML services (SageMaker, Vertex AI)
- Research Team Collaboration: Experience working with data science or research teams, effectively translating experimental code into production systems
- ML Infrastructure: Experience with MLOps tools (MLflow, Kubeflow), container technologies (Docker, Kubernetes), inference engines (vLLM, SGLang), distributed computing (), and data labeling platforms (Label Studio)
- Software Engineering: Strong foundation in version control, testing strategies, software architecture principles, async programming, and concurrent system design
- Work with a groundbreaking AI platform solving real enterprise pain points
- Help clients achieve measurable ROI through next-gen automation
- Join a remote-first, globally distributed team backed by industry leaders
- Shape the success function and influence product direction in a fast-scaling AI company
