ML Ops Engineer

Role: ML Ops Engineer

Location: hybrid/remotely

Rate: up to 180 PLN/h netto + VAT (B2B)

Duration: long-term cooperation

Type of contract: B2B

Industry: IT/Insurance

We are seeking a skilled ML Ops Engineer focused on operationalizing AI models in production environments. The candidate will be responsible for deploying, monitoring, and maintaining AI/ML solutions, ensuring they seamlessly integrate into business operations. Proficiency in Python, along with experience in cloud services and containerization technologies, are essential for success in this role.

Main Responsibilities:

• Model Operationalization: Package, deploy, and monitor machine learning models in production environments using Azure ML, MLflow, and containerization technologies.
• Infrastructure & Deployment: Design and maintain scalable infrastructure using Azure Kubernetes Service (AKS), Docker, and CI/CD pipelines.
• Model Serving: Build APIs, endpoints, and inference pipelines to serve models efficiently and reliably.
• Monitoring & Observability: Implement monitoring, logging, and alerting for ML services to ensure uptime, performance, and model accuracy.
• Versioning & Governance: Manage the versioning of models, artifacts, and configurations, ensuring traceability and compliance with governance standards.
• Collaboration: Work closely with Data Scientists to understand model requirements and with DevOps teams to integrate ML workflows into broader CI/CD frameworks.
• Automation: Drive automation across the ML lifecycle, including testing, deployment, retraining triggers, and rollback mechanisms.
• Security & Compliance: Ensure that AI solutions meet enterprise security, compliance, and data privacy requirements.
• Code Quality & Engineering: Write clean, efficient, and maintainable Python code, following modern software engineering practices including testing, version control, and documentation.

Key Requirements:

• Python Proficiency: Expert-level knowledge of Python, particularly in building production-grade software and ML/AI pipelines.
• Software Engineering Background: 3+ years of experience as a software developer or backend engineer, ideally with exposure to cloud-native and distributed systems.
• Cloud & DevOps Expertise: Hands-on experience with Azure ML, Azure DevOps, Azure Container Registry, and Azure Key Vault.
• Containerization & Orchestration: Proficiency in Docker and Kubernetes (AKS) for deploying scalable ML solutions.
• CI/CD for ML: Experience building and maintaining CI/CD pipelines for ML applications, including automated testing and deployment.
• Monitoring & Logging: Familiarity with tools such as Prometheus, Grafana, Azure Monitor, or similar.
• ML Lifecycle Tools: Experience with MLflow or similar tools for experiment tracking, model registry, and reproducibility.
• Collaboration in Agile Teams: Proven ability to work effectively within cross-functional teams in an Agile/Scrum setup.
• Language Skills: Fluent in English, both written and spoken (minimum B2 level).

Nice to Have:

• Experience with Databricks or other managed ML platforms.
• Familiarity with model drift detection and automated retraining pipelines.
• Knowledge of vector databases and inference optimization.
• Understanding of AI-specific security and compliance practices.

Other Details:

This position collaborates with Data Scientists and DevOps teams, focusing on operational excellence and production-readiness. If you are passionate about deploying and managing real-world ML applications and enjoy working with production systems, automation, and cloud-native tools, this is the role for you.