MLOps Engineer

About the role

As a MLOps Engineer within the SN AI Lab, you will join a team of engineers designing, deploying, and scaling innovative AI solutions in cloud environments. You will bridge machine learning development and production operations, ensuring AI systems are reliable, secure, scalable, and aligned with business needs.

Key responsibilities

• Design, build, and maintain scalable MLOps platforms, frameworks, and deployment pipelines supporting reliable delivery of machine learning and generative AI solutions.
• Develop and integrate cloud‑native services, automation workflows, and CI/CD practices to improve operational efficiency, system reliability, and deployment velocity.
• Implement monitoring, observability, tracing, and performance management capabilities to proactively identify issues and optimise production systems.
• Drive process optimisation initiatives that improve model lifecycle management, operational resilience, governance, and overall team effectiveness.
• Contribute to data security, compliance, and governance standards by ensuring appropriate controls, monitoring, and responsible management of AI and data assets.
• Support knowledge sharing, technical storytelling, and documentation to increase transparency, adoption, and understanding of AI capabilities across the organisation.
• Mentor and support junior engineers, helping develop MLOps and AI engineering capabilities across the team.

Required profile

• A degree in Software Engineering, Computer Science, Artificial Intelligence, or a related technical field.
• Strong experience developing software solutions using Python and modern engineering practices.
• Experience with GitHub, Docker, and machine learning frameworks such as PyTorch or TensorFlow.
• Hands‑on experience working with cloud platforms such as Azure, AWS, or GCP.
• Experience building APIs and services using FastAPI or similar frameworks.
• Knowledge of CI/CD pipelines, automated testing frameworks, and GitHub Actions.
• Experience implementing observability, monitoring, and tracing solutions for AI/ML applications, including tools such as Langfuse or equivalent.
• Understanding of machine learning lifecycle management, deployment strategies, and production monitoring.

Required skills

• Python
• GitHub
• Docker
• PyTorch
• TensorFlow
• Azure
• AWS
• GCP
• FastAPI
• CI/CD pipelines
• GitHub Actions
• Observability tools (e.g., Langfuse)