We are seeking a skilled and proactive MLOps Engineer to support the development, deployment, and maintenance of machine learning solutions in production environments. This role bridges the gap between data science and operations by implementing robust MLOps practices, automating workflows, and ensuring the scalability and reliability of ML systems. You will collaborate with cross-functional teams to ensure seamless integration of models into existing infrastructure and drive continuous improvement in ML lifecycle management.

Roles and Responsibilities

• Design, build, and deploy scalable ML pipelines for training and inference (batch and real-time).

• Integrate MLOps practices within DevOps workflows including CI/CD for ML models.

• Automate model deployment and monitoring using tools such as Azure ML or similar platforms.

• Ensure reliability and performance of models in production, addressing model drift and accuracy.

• Create and maintain scripts and frameworks to support automation and reproducibility.

• Monitor and troubleshoot production ML systems, perform root cause analysis, and implement preventive measures.

• Collaborate with Data Scientists, Engineers, and Architects to translate requirements into production-grade systems.

• Support platform optimization, cost monitoring, and infrastructure scaling.

• Contribute to documentation and best practices in MLOps and ML lifecycle management.

Qualifications and Skills

• 5 +  years of experience in MLOps, Platform Engineering, or DevOps, with exposure to machine learning.

• Proven experience deploying and maintaining ML models in production environments.

• Strong proficiency with cloud platforms (e.g., Azure, AWS, GCP) and ML tools (e.g., Azure ML, SageMaker).

• Solid understanding of Python, SQL, and data pipeline orchestration (e.g., Airflow, Prefect).

• Experience with version control, CI/CD pipelines, and Infrastructure as Code (e.g., Terraform, ARM).

• Familiarity with monitoring tools for performance tracking and alerting.

Education

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field.

Nice-to-Haves

• Experience with containerization (Docker, Kubernetes) for model deployment.

• Knowledge of ML model testing strategies and performance tuning.

• Experience with data engineering tools and ETL frameworks.

• Familiarity with budget and cost estimation in cloud environments.

• Understanding of security and compliance in cloud-based ML solutions.