Lead MLOps

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)