Machine Learning Engineer

MLOps Engineer – Software Division

100% on-site work required – no remote/hybrid options.

We are looking for a skilled and motivated MLOps Engineer to join our software division. This role offers the opportunity to work at the forefront of AI and Big Data technologies, supporting cutting-edge machine learning systems in a dynamic, mission-critical environment.

Job Responsibilities:

• Design, develop, and manage infrastructure for deploying and monitoring machine learning models.
• Collaborate with data scientists, software engineers, developers, and system architects to ensure stable and efficient model training and deployment.
• Build and maintain scalable Big Data infrastructure using industry-standard tools.
• Develop and maintain CI/CD/CT pipelines for model training, validation, and deployment.
• Ensure compliance with data privacy and security standards.
• Monitor deployed models and troubleshoot performance issues, ensuring long-term consistency and reliability.
• Develop automation tools for data preparation, feature engineering, and model training workflows.
• Implement version control and model management practices to ensure traceability and reproducibility.
• Provide training and mentorship to engineers and data professionals on MLOps best practices.
• Stay current with emerging trends and technologies in AI, machine learning, Big Data, and cloud infrastructure.

Requirements:

• Relevant academic degree in computer science, engineering, or a related field – mandatory.
• Minimum 2 years of experience in MLOps, machine learning, or a closely related field – mandatory.
• Proficiency in Python and ML libraries such as TensorFlow, PyTorch, scikit-learn – mandatory.
• In-depth knowledge of DevOps tools: Docker, Podman, Kubernetes, Jenkins, Terraform, Ansible – mandatory.
• Hands-on experience with CI/CD tools (e.g., GitHub, GitLab, Azure DevOps) and Git – mandatory.
• Understanding of model deployment, monitoring, and performance optimization – mandatory.
• Familiarity with data storage systems: SQL, NoSQL, Data Lakehouse – mandatory.
• Experience with cloud platforms (GCP, AWS, Azure) and managing cloud resources for ML – advantage.
• Experience with MLOps platforms: ClearML, Kubeflow, MLflow – advantage.
• Familiarity with ML frameworks for model serving: TensorFlow Serving, TorchServe, Seldon – advantage.
• Experience with Apache Airflow – advantage.
• Familiarity with monitoring tools: Prometheus, Grafana – advantage.
• Experience working with Data Lakehouse technologies: Cloudera, Hive, Spark, Kafka – advantage.