MLOps Engineer

• Strong proficiency in Python, including production-grade development and experience with ML and automation libraries
• 2+ years of MLOps experience deploying, operating, and maintaining machine learning models in production
• Understanding of the ML model lifecycle, machine learning algorithms, feature engineering, and model monitoring (drift detection, performance tracking)
• Experience with data and experiment versioning tools such as MLflow, DVC, and Weights & Biases
• Hands-on experience with cloud platforms: Azure ML, Azure DevOps Pipelines, ACR; AWS EC2, S3, IAM, Networking; GCP
• Experience building and maintaining CI/CD pipelines (GitHub Actions, Azure DevOps, GitLab CI)
• Understanding of Infrastructure as Code principles and tools such as Terraform
• Experience with Docker and container orchestration technologies (Kubernetes, Amazon EKS, ECS)
• Strong SQL skills (MS SQL, PostgreSQL), including writing complex queries and data manipulation
• English proficiency at B2 level or higher

Nice to have:

• Experience with LLM- and Generative AI-based solutions in production
• Experience with PySpark, Databricks, and Big Data technologies
• Understanding of networking concepts, security configurations, and virtual private networks
• Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or related field

• Design, develop, and maintain end-to-end ML pipelines (data → training → deployment → monitoring)
• Automate infrastructure for model training and inference workloads in cloud environments
• Implement CI/CD/CT practices for code, data, and machine learning models
• Ensure the reliability, scalability, and security of AI services in production
• Collaborate closely with Data Scientists and DevOps Engineers to transform prototypes into production-ready solutions