Senior MLOps

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September 17

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Complexio

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Artificial Intelligence • SaaS • Enterprise

Complexio is a company specializing in foundational artificial intelligence solutions designed for task automation using whole company data. Headquartered in London, they also have offices in Copenhagen and Monaco. Complexio is focused on leveraging AI technologies to enhance efficiency and automate processes within organizations. They prioritize data privacy and consent management, ensuring compliance with international data protection standards.

11 - 50 employees

🤖 Artificial Intelligence

☁️ SaaS

🏢 Enterprise

📋 Description

• Complexio’s Foundational AI platform automates enterprise processes by ingesting and understanding structured and unstructured data • Bridge the gap between data science research and production-ready ML systems; write production Python, design cloud architectures, and collaborate with research teams • Design, build, and maintain end-to-end ML pipelines from data ingestion to model deployment and monitoring • Architect and manage scalable cloud infrastructure for ML workloads, including container orchestration and automated testing • Partner closely with data scientists and research teams to translate experimental models into robust, production-ready systems • Establish infrastructure as code, CI/CD pipelines, automated deployments, and comprehensive logging/monitoring • Debug Kubernetes deployments, optimise training pipelines, and explain technical trade-offs to data scientists

🎯 Requirements

• 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

🏖️ Benefits

• 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