We are looking for an MLOps Engineer to join the Data, AI & ML teams of a leading financial organization.

Direct employment – join the company from day one.

This role combines Machine Learning Engineering, MLOps and Cloud Infrastructure, with a strong focus on building, deploying, operating and continuously improving ML solutions in production, alongside supporting GenAI solutions.

What will you do?

• Design and implement architectures for Machine Learning solutions.
• Lead the deployment of ML models and solutions into production.
• Develop and maintain MLOps and CI/CD pipelines.
• Build and maintain infrastructure that enables Data Science teams to develop, deploy and operate models efficiently and reliably.
• Establish scalable processes for deployment, versioning, monitoring and lifecycle management of ML models.
• Develop and maintain services and APIs supporting Machine Learning solutions.
• Contribute to the development of the organization's ML platform and its evolution.
• Work closely with Data Scientists, Data Engineers, DevOps, Architecture, Cyber Security and Infrastructure teams.
• Support the implementation and operationalization of GenAI solutions.

Requirements

• 3+ years of experience in MLOps, ML Engineering, or roles involving the deployment and operation of Machine Learning models in production.
• Significant hands-on experience with AWS.
• Strong development experience with Python and Bash.
• Experience working with Docker.
• Experience with Git.
• Experience with CI/CD – GitHub Actions is a significant advantage.
• Experience developing and maintaining services / APIs supporting ML models.
• Hands-on experience with ML deployment, versioning and monitoring processes.
• Ability to work effectively with Data Scientists, Data Engineers and DevOps teams.
• Strong technical ownership and the ability to lead ML solutions from architecture and design through production deployment.

Why join us?

This is an opportunity to play a key role in building and evolving an enterprise-scale ML platform, working at the intersection of AI, Machine Learning, Cloud and modern software engineering in a complex financial environment.