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ML Ops Engineer

Remote:

Full Remote

Contract:

Fixed term

Experience:

Mid-level (2-5 years)

Work from:

India

Offer summary

Qualifications:

Bachelor’s or Master’s in Computer Science, Data Science, or related field., 3+ years experience in ML Ops or DevOps roles., Strong understanding of machine learning lifecycle management., Familiarity with containerization tools like Docker and Kubernetes., Experience with cloud platforms like AWS, Azure, or GCP..

Key responsabilities:

Develop automated pipelines for model deployment, monitoring, and scaling.
Implement best practices for model version control and performance optimization.
Collaborate with DevOps and data science teams to integrate ML Ops processes.
Establish monitoring frameworks for model performance and anomalies.
Create documentation for ML Ops processes and ensure compliance.

Awign Information Technology & Services Scaleup https://www.awign.com/

201 - 500 Employees

See more Awign offers

Job description

Your missions

About Awign Expert:

Awign Expert, a division of Awign - India's largest work-as-a-service platform. We connect skilled professionals with exciting project-based opportunities from top companies, handling onboarding, feedback, conflict resolution, and payroll. Our mission is to empower professionals to focus on their work by managing administrative tasks, providing access to a network of renowned companies and rewarding assignments

ML Ops Engineer

Duration: 6 Months

Location: Remote

Experience Required: 3+ years

Job Overview:

Lead the strategic implementation and management of Machine Learning Operations (ML Ops) to streamline the deployment and maintenance of machine learning models, ensuring their seamless integration into production environments. Collaborate closely with data scientists and IT teams to bridge the gap between development and operations, enhancing the efficiency and reliability of ML workflows.

Key Responsibilities:

Model Deployment and Automation:

Develop and maintain automated pipelines for model deployment, monitoring, and scaling, ensuring efficient and robust integration into production systems.
Implement best practices for model version control, model tracking, and performance optimization to ensure reliability and reproducibility of ML models.

ML Ops Integration and Collaboration:

Collaborate with DevOps teams to integrate ML Ops processes into existing CI/CD pipelines, facilitating continuous deployment and rapid iteration of ML models.
Work closely with data scientists to streamline the model development lifecycle, from experimentation to production deployment.

Monitoring and Performance Management:

Establish monitoring frameworks to track model performance and detect anomalies, ensuring optimal functioning of ML models in production.
Develop and implement strategies for model retraining and updates based on performance metrics and feedback.

Documentation and Compliance:

Create and maintain comprehensive documentation for ML Ops processes, including deployment pipelines, monitoring tools, and model performance metrics.
Ensure adherence to compliance standards and data security protocols throughout the ML lifecycle.

Qualifications:

Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
Proven experience in ML Ops or DevOps roles, with a strong understanding of machine learning lifecycle management.
Familiarity with containerization and orchestration tools like Docker and Kubernetes.

Preferred Skills:

Experience with cloud platforms (e.g., AWS, Azure, GCP) for deploying and managing ML models.
Proficiency with DevOps tools such as Kubernetes, Docker, and Jenkins for CI/CD pipeline integration.
Strong understanding of infrastructure-as-code (IaC) tools and principles to automate infrastructure setup and management.