Machine Learning Ops and Data Engineer

Málaga / HybridHybrid

Machine LearningData & AI

0 views0 saves0 applied

Apply Now

Quick Summary

Key Responsibilities

Deploy machine learning models into production environments and support their operational lifecycle. Support cloud-based analytical, reporting, and machine learning infrastructure.

Technical Tools

Machine LearningData & AI

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. Fluent in Polish.