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The role involves deploying and monitoring machine learning models in production while maintaining scalable ETL pipelines and data platform infrastructure. It requires collaborating with cross-functional teams to ensure data availability and optimize the operational lifecycle of ML solutions.
Candidates need 2+ years of experience in data engineering and ML, with strong Python and SQL skills. Proficiency in cloud platforms (preferably Azure), containerization, and DevOps practices is required.
This role combines data platform engineering and MLOps responsibilities, supporting both reliable analytical data flows and production-ready machine learning solutions. The person will maintain and improve ETL pipelines, support the data platform environment, and help deploy, monitor, and optimize machine learning models in production. The role requires close cooperation with Data Science, Engineering, BI, Risk, and IT teams to ensure that data and model processes are scalable, well-documented, stable, and aligned with business needs.
As a Data Platform and MLOps engineer, you will be part of our Data Platform team and play an important role in our daily operations. Your responsibilities will include:
Deploy machine learning models into production environments and support their operational lifecycle.
Support cloud-based analytical, reporting, and machine learning infrastructure.
Collaborate closely with Data Science, Engineering, Risk, BI, and IT teams to align data and model requirements with production standards.
Develop automation for model deployment, updates, scaling, and recurring data processing tasks.
Implement monitoring for both data pipelines and machine learning models, including performance, availability, and quality checks.
Ensure reliable operation and continuous development of the analytical data warehouse environment.
Design, maintain, troubleshoot, and optimize ETL/data pipelines supporting reporting, analytics, and machine learning use cases.
Ensure timely and high-quality data availability for BI, Risk, Data Science, and other business stakeholders.
Identify, investigate, and resolve performance issues across data warehouse, ETL, and model deployment processes.
Troubleshoot, debug, upgrade, and improve existing software, pipelines, and deployment processes.
Gather and evaluate user feedback, recommend improvements, and execute enhancements.
Maintain technical documentation for data processes, model deployments, configurations, and operational procedures.
We are looking for someone who has:
2+ years of experience in data engineering and machine learning
Strong Python programming skills and intermediate SQL knowledge
Good understanding of databases, data warehouse concepts, and ETL processes
Understanding of machine learning lifecycle and model operationalization
Using LLM’s to generate and optimize code, ability to use AI platform features to enhance and speed up workflows
Knowledge of DevOps practices, CI/CD pipelines, and version control
Experience with cloud-based analytical and reporting solutions, preferably Azure
Familiarity with machine learning frameworks and tools such as scikit-learn and XGBoost
Familiarity with containerization technologies such as Docker
Ability to monitor, troubleshoot, and optimize data pipelines, infrastructure, and deployed models
Experience with software design, development, debugging, and documentation
Proficient English, B1/B2 level or higher.
A friendly and collaborative team culture
The opportunity to learn from experienced colleagues and grow within IT
A modern technical environment with room for improvement and innovation
A workplace where teamwork, curiosity, and continuous improvement are valued
Helping brands own the payment experience across Northern Europe
Avarda offers secure and flexible omnichannel payment services for retailers. Our payment services will strenghten retailer's brand awareness, increase customer loyalty, conversion and add upsell possibilities. Avarda was founded in 2015 as a joint venture between TF Bank and Intrum. Since November 2017, we are 100 % owned subsidiary of TF Bank AB, which is listed in Nasdaq Stockholm. With TF Bank's strong background and experience in mail order sales finance, we know what it requires to build strong customer relationships with retailer's customers. Combining increased brand awareness, increased loyalty and conversion with upsell possibilities and long experience in Sales Finance, we know how to help retailers to grow and achieve their goals. By following us, you can stay up to date on what's going on with us, such as news, vacancies and industry trends.
Offices: Barnhusgatan 16, Stockholm, Stockholm County 11123, SE · Keskuskatu 6, Helsinki, Southern Finland 00100, FI
