MLOps Engineer

4 weeks ago

Anywhere in IndiaMultiple Locations, IN GOOGNU DATA SERVICES PRIVATE LIMITED Full time

Job Description :

- Design and Implement MLOps Infrastructure: Build and maintain robust MLOps workflows using Azure Machine Learning and other Azure services to automate machine learning lifecycle from data preparation to model deployment and monitoring.

- Collaborate with Cross-Functional Teams: Work closely with data scientists, software developers, and IT specialists to ensure seamless integration and deployment of ML models into production.

- Continuous Monitoring and Optimization: Monitor the performance of machine learning models in production, identify issues with model drift, and optimize them for performance and scalability.

- Data Governance and Security: Implement and enforce data governance and security policies compliant with industry standards, ensuring data integrity and privacy.

- Research and Development: Stay abreast of the latest developments in MLOps technologies and Azure services. Evaluate and adopt new tools and practices to improve the deployment process and model performance.

- Mentorship and Leadership: Provide guidance and mentorship to junior MLOps engineers and team members, fostering a culture of learning and continuous improvement.

Qualifications :

Education : Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.

Professional Experience: Minimum of 6 years of experience in a role focusing on machine learning, data engineering, or software development, with at least 3 years dedicated to MLOps.

Technical Skills :

- Proficiency in Azure Machine Learning, Azure Data Factory, Azure Databricks, and other relevant Azure services.

- Strong experience in automating and orchestrating ML workflows at scale.

- Knowledge of Python, SQL, and other scripting languages commonly used in data processing.

- Familiarity with containerization technologies such as Docker and orchestration tools like Kubernetes.

- Experience with CI/CD tools and practices in a machine learning context.

(ref:hirist.tech)

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