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Complexio’s Foundational AI automates business activities by ingesting structured and unstructured company-wide data to extract meaningful insights. Our proprietary models and algorithms analyse human interactions with data, enabling automation to replicate and enhance these processes independently.
Complexio is a joint venture between Hafnia and Símbolo, in partnership with Marfin Management, C Transport Maritime, Trans Sea Transport, and BW Epic Kosan.
We are looking for an MLOps Engineer to design, deploy, and optimize machine learning infrastructure across on-premises and multi-cloud environments (AWS, Azure, Google Cloud). You will be responsible for ensuring the smooth deployment, monitoring, and scaling of AI/ML models while managing data pipelines and GPU-powered workloads.
Key Responsibilities
- Deploy and manage ML models in production environments, ensuring scalability and reliability.
- Design and maintain ML pipelines, automating training, validation, and deployment workflows.
- Optimize AI/ML infrastructure for performance, cost, and efficiency across cloud and on-premise systems.
- Integrate and manage vector and graph databases (e.g., Neo4j, Pinecone, Milvus) for AI-driven applications.
- Implement observability & monitoring solutions for model performance, data drift, and system health.
- Ensure compliance with security and data governance best practices in ML deployment.
- Collaborate with Data Scientists and DevOps teams to streamline AI model lifecycle management.
Experience: 7+ years in ML infrastructure, DevOps, or Cloud Engineering.
ML & Cloud Stack:
- Hands-on experience with Kubernetes, Docker, and containerised ML workloads.
- Strong expertise in AWS, Azure, or Google Cloud, with knowledge of GPU-based computing.
- Experience in CI/CD pipelines for machine learning (e.g., GitHub Actions, MLflow, Kubeflow).
Programming:
- Proficiency in Python (experience with Go or Java is a plus).
- Strong experience in scripting & automation (Bash, Terraform, Ansible).
Databases & Storage:
- Knowledge of vector & graph databases (e.g., Neo4j, Milvus, Pinecone).
- Experience managing distributed data storage & processing.
Bonus Points
Experience deploying LLMs & NLP models in production.
Familiarity with feature stores and model versioning.
Experience with edge AI deployments and federated learning.
- 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.
- (Remote must be within 4-5 hours of CET timezone)
