Machine Learning Ops and Data Engineer at Avarda Group

Tallinn, Harju, Estonia -

Full Time

Start Date

Immediate

Expiry Date

07 Oct, 26

Salary

0.0

Posted On

09 Jul, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, SQL, MLOps, Data Engineering, ETL Pipelines, Azure, Docker, CI/CD, Scikit-learn, XGBoost, Data Warehousing, Model Deployment, Monitoring, Software Design, Version Control, LLM Optimization

Industry

Financial Services

Description

About the role 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. Key Responsibilities 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. Qualifications and Experience 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. What we Offer 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

Responsibilities

The role involves deploying and monitoring machine learning models in production while maintaining scalable ETL pipelines and data warehouse environments. It requires collaborating with cross-functional teams to ensure data availability and optimize the operational lifecycle of ML solutions.