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 includeProduction ML Pipeline Development: Design, build, and maintain end-to-end ML pipelines from data ingestion to model deployment and monitoringInfrastructure Management: Architect and manage scalable cloud infrastructure for ML workloads, including container orchestration and automated testingResearch Collaboration: Partner closely with data scientists and research teams to translate experimental models into robust, production-ready systemsDevOps Best Practices: Establish infrastructure as code, CI/CD pipelines, automated deployments, and comprehensive logging/monitoringAdvanced Python Programming: Production Python experience with web frameworks (FastAPI, Flask), testing frameworks, and ML libraries (PyTorch, scikit-learn, numpy) a great-to-haveCloud 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 systemsML 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 designWork with a groundbreaking AI platform solving real enterprise pain pointsHelp clients achieve measurable ROI through next-gen automationJoin a remote-first, globally distributed team backed by industry leadersShape the success function and influence product direction in a fast-scaling AI company

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