Role Overview
We are looking for a skilled MLOps Engineer to design, deploy, and manage scalable machine learning pipelines on Azure cloud. The ideal candidate will work closely with Data Scientists, DevOps, and IT teams to ensure seamless deployment, monitoring, and optimization of ML models in production environments.
Key Responsibilities
- Design, implement, and manage scalable ML pipelines using Azure ML, Databricks, and PySpark
- Build and maintain CI/CD pipelines using Bitbucket and Jenkins, integrating SonarQube for code quality
- Deploy and manage ML models using Azure Kubernetes Service (AKS)
- Develop reusable templates to streamline ML deployment processes
- Design and manage APIs for integration of ML models with applications
- Monitor model performance including data drift, retraining, and data validation
- Optimize pipelines for performance, scalability, and cost efficiency
- Implement cost monitoring strategies for cloud resource utilization
- Collaborate with cross-functional teams for model deployment and lifecycle management
- Maintain proper documentation for workflows, pipelines, and processes
Required Skills & Qualifications
- 4–6 years of experience in MLOps / Machine Learning Engineering
- 2–3 years of hands-on experience with Azure MLOps ecosystem
- Strong experience with:
- Azure ML
- Databricks
- PySpark
- Experience with CI/CD tools such as Jenkins and Bitbucket
- Knowledge of containerization and orchestration (AKS / Kubernetes)
- Strong understanding of API development and integration
- Experience in model monitoring, data drift detection, and pipeline optimization
- Good understanding of cloud cost optimization strategies
- Strong problem-solving and analytical skills
- Good communication and collaboration abilities
