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Senior MLOPS at Complexio. 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 (. Ray.io. ), and data labeling platforms (Label Studio). Software Engineering: Strong foundation in version control, testing strategies, software architecture principles, async programming, and concurrent system design. Company Location: United Kingdom.