This is a remote position. We are looking for an MLOps Engineer to support an enterprise analytics and optimisation platform within an international banking environment. The platform underpins pricing, capital allocation, and customer lifetime value decisions across multiple markets and product lines. It operates at scale with regular model retraining cycles and governed analytics processes. Analytical outputs feed both traditional reporting layers and GenAI workflows that generate automated insights and scenario analysis. This role focuses on operationalising analytical models and ensuring stable, repeatable production workflows.
Responsibilities:
Design and operate training and deployment pipelines for analytical and optimisation models
Automate model retraining, validation, and promotion processes
Ensure reproducibility and consistency across development, testing, and production environments
Support scalable analytical workloads across cloud platforms
Enable structured exposure of model outputs to GenAI workflows
Monitor performance, stability, and reliability of ML pipelines
Collaborate closely with data scientists and analytics teams to streamline experimentation to production
Requirements:
Strong experience in MLOps or ML platform engineering
Solid Python skills for automation and tooling
Hands on experience with Docker and Kubernetes
Practical experience with MLflow or similar model lifecycle management tools
Experience with workflow orchestration tools such as Airflow
Hands on experience with CI/CD pipelines
Experience working with cloud data platforms
Strong understanding of reproducibility and environment management
Fluent English for professional collaboration
Nice to have:
Experience integrating ML outputs with LangChain or LangGraph workflows
Exposure to banking, finance, or regulated environments
Experience with optimisation models or large scale analytical platforms
Understanding of data governance and audit requirements
Benefits:
Solid, competitive salary
Work in a multinational environment on international projects
Comprehensive healthcare
Long-term B2B contract with a stable project pipeline
Remote work model
MLOps Engineer
This is a remote position. We are looking for an MLOps Engineer to support an enterprise analytics and optimisation platform within an international banking environment. The platform underpins pricing, capital allocation, and customer lifetime value decisions across multiple markets and product lines. It operates at scale with regular model retraining cycles and governed analytics processes. Analytical outputs feed both traditional reporting layers and GenAI workflows that generate automated insights and scenario analysis. This role focuses on operationalising analytical models and ensuring stable, repeatable production workflows.
Responsibilities:
Design and operate training and deployment pipelines for analytical and optimisation models
Automate model retraining, validation, and promotion processes
Ensure reproducibility and consistency across development, testing, and production environments
Support scalable analytical workloads across cloud platforms
Enable structured exposure of model outputs to GenAI workflows
Monitor performance, stability, and reliability of ML pipelines
Collaborate closely with data scientists and analytics teams to streamline experimentation to production
Requirements:
Strong experience in MLOps or ML platform engineering
Solid Python skills for automation and tooling
Hands on experience with Docker and Kubernetes
Practical experience with MLflow or similar model lifecycle management tools
Experience with workflow orchestration tools such as Airflow
Hands on experience with CI/CD pipelines
Experience working with cloud data platforms
Strong understanding of reproducibility and environment management
Fluent English for professional collaboration
Nice to have:
Experience integrating ML outputs with LangChain or LangGraph workflows
Exposure to banking, finance, or regulated environments
Experience with optimisation models or large scale analytical platforms
Understanding of data governance and audit requirements
Benefits:
Solid, competitive salary
Work in a multinational environment on international projects
Comprehensive healthcare
Long-term B2B contract with a stable project pipeline
Remote work model
What You'll Do
Design and operate training and deployment pipelines for analytical and optimisation models
Automate model retraining, validation, and promotion processes
Ensure reproducibility and consistency across development, testing, and production environments
Support scalable analytical workloads across cloud platforms
Enable structured exposure of model outputs to GenAI workflows
Monitor performance, stability, and reliability of ML pipelines
