Job Description

The main responsibilities of the position include:

• Deploy machine learning models and integrate AI tools into production systems.
• Optimise and fine-tune models for performance, scalability, and efficiency.
• Collaborate with data engineers and data scientists to develop and maintain data pipelines, working with large datasets to prepare data for model training and evaluation.
• Stay up to date with the latest advancements in machine learning and AI technologies.
• Implement MLOps best practices for continuous integration, deployment, and monitoring of ML models.
• Set up pipelines for A/B testing of different model variations to evaluate performance and enhance model effectiveness.

Main requirements:

• BSc/MSc in Computer Science, Data Science, Machine Learning, or a related field.
• 3+ years of experience in machine learning/AI and DevOps/MLOps.
• Strong programming background with proven experience in Python and other languages.
• Experience with data structures, algorithms and ETL/ELT procedures.
• Experience with machine learning libraries such as TensorFlow, PyTorch, and Scikit-learn.
• Knowledge of containerization and model deployment using Docker.
• Experience working with CI/CD pipelines using tools like Jenkins and GitLab CI.
• Familiarity with task orchestration tools such as Airflow, Kubeflow, and MLflow.
• Understanding of cloud computing platforms (AWS, GCP, or Azure) and big data ecosystems.
• Experience with monitoring and logging solutions such as Prometheus, Grafana, CloudWatch, and the ELK Stack.
• Strong problem-solving skills and the ability to work in a collaborative team environment.
• Fluency in English Language

Benefit from:

• Attractive remuneration package
• Private health insurance
• Corporate pension fund
• Intellectually stimulating work environment
• Continuous personal development and international training opportunities

The Hiring Experience: What Awaits You

• Let’s Connect – Intro Chat with Talent Acquisition
• Deep Dive – First Interview with Your Future Team
• Bring It to Life – Role-Specific Take-Home Task
• Final Connection – Final Interview