MLOps Engineer

Our Expectations:

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

Nice to Have:

• Experience working with LLM- and Generative AI-based solutions in production environments;
• Experience with PySpark, Databricks, and other Big Data technologies for distributed data processing;
• Understanding of networking concepts, security configurations, and virtual private networks;
• Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or a 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] Requirements: Python, MLOps, Machine learning, MLflow, Cloud platform, Azure ML, Azure DevOps, AWS EC2, AWS S3, IAM, Networking, GCP, CD pipelines, GitHub Actions, GitLab CI, Infrastructure as Code, Terraform, Docker, Kubernetes, Amazon EKS, ECS, AWS ECS, Data management, SQL, PostgreSQL, PySpark, Databricks, Big Data, Security, Virtual private network, Degree