Machine Learning / MLOps Engineer

We are seeking a highly technical and collaborative Machine Learning / MLOps Engineer to join our Data Science team at UST Global. This role bridges the gap between data science and software engineering, focusing on the full ML lifecycle from model development and deployment to monitoring and optimization. The ideal candidate will have strong expertise in Large Language Models (LLMs), cloud-based deployment, and MLOps best practices, ensuring scalable, reliable, and production-ready AI solutions.

Location: Ukraine, Remote

Key Responsibilities:

• Design, build, and maintain end-to-end machine learning pipelines (data ingestion, training, deployment, and monitoring).
• Collaborate with data scientists to operationalize ML models and ensure reproducibility, scalability, and high performance.
• Implement MLOps frameworks and CI/CD pipelines for automated model deployment, versioning, and governance.
• Optimize and maintain LLM-based applications, including fine-tuning, inference optimization, and integration with downstream systems.
• Manage cloud infrastructure (AWS, Azure, or GCP) for ML workloads and ensure cost-efficient scaling.
• Develop and enforce best practices for ML lifecycle management, including model registry, performance tracking, and retraining strategies.
• Monitor and troubleshoot production models to ensure robustness, reliability, and compliance.
• Work closely with cross-functional teams (Data Engineering, DevOps, Software Engineering) to integrate AI solutions into production environments.

Requirements:

• Proven experience in end-to-end ML lifecycle management and MLOps (from data prep to deployment and monitoring).
• Hands-on expertise with Large Language Models (LLMs) and modern ML frameworks (TensorFlow, PyTorch, Hugging Face, etc.).
• Strong programming skills in Python, and familiarity with libraries such as scikit-learn, MLflow, Airflow, or Kubeflow.
• Experience with containerization and orchestration tools (Docker, Kubernetes).
• Demonstrated ability to deploy ML models in cloud environments (AWS SageMaker, Azure ML, or GCP Vertex AI).
• Strong understanding of API design, CI/CD pipelines, and infrastructure as code (Terraform, CloudFormation).

Working Conditions / Benefits:

• Friendly professional staff and a warm atmosphere.
• An environment where you can implement your ideas.
• Paid vacations and sick leaves.
• Medical insurance.
• Opportunity to work remotely.
• Participation in educational activities and thematic conferences.
• Team parties and corporate events.
• Global exposure across UST’s AI and Data Science projects.
• Collaborative culture focused on innovation and delivery excellence.