Machine Learning Operation Engineer

Bachelor's degree in Computer Science, Engineering, or a related Technical field preferred.

5–8 years of overall IT experience with at least 3–5 years of relevant MLOps/Machine Learning Engineering experience.

Proven experience in Machine Learning Engineering and Machine Learning Operations (MLOps).

Strong understanding of machine learning concepts, model lifecycle management, model governance, model performance monitoring, and integration.

Hands-on experience in model deployment, monitoring, profiling, governance, and performance analysis.

Strong Python programming skills with experience in ML frameworks such as TensorFlow and PyTorch.

Experience with cloud platforms such as AWS, Azure, or GCP and cloud-native ML services.

Experience in building and automating end-to-end machine learning pipelines.

Knowledge of Agile and Waterfall development methodologies.

Excellent analytical, problem-solving, verbal, and written communication skills.

Collaborate with business and technical stakeholders to design scalable machine learning solutions using cloud platforms such as Azure and Snowflake.

Design, develop, and maintain machine learning pipelines and frameworks to support enterprise analytics and reporting requirements.

Work closely with Data Science, ML Engineering, and Data Quality teams to implement efficient model deployment strategies.

Configure, manage, and optimize cloud environments and machine learning services.

Implement enhancements for model integration, storage, profiling, monitoring, processing, governance, and archival.

Automate ML workflows to improve model reliability, scalability, and operational efficiency.

Monitor model performance, troubleshoot production issues, and ensure compliance with governance standards.

Recommend platform improvements and emerging technologies to support strategic business and technology objectives.

Ensure adherence to coding standards, security policies, and industry best practices throughout the ML lifecycle.