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    MLOps Engineer - Bengaluru, India - Change leaders

    Change leaders
    Change leaders Bengaluru, India

    5 days ago

    Default job background
    Description

    Skills Set :
    Python, ML concepts and frameworks, Fast API, Graph QL, AWS, ML Flow, AirFlow, ML pipeline creation, drift monitoring.

    Strong understating of Python, ML concepts and frameworks, Fast API, Graph QL.

    • Experience in developing scalable APIs.
    • Knowledge of AWS, preferred services are storage, EC2, Kubernetes.
    • Exposure of ML best practices, documentation and unit testing.
    • ML Flow, AirFlow, ML pipeline creation, drift monitoring and control.
    • Experience in developing and deploying machine learning models in a production environment using CI/CD.
    • Communicate with clients to understand requirements and ask right questions.
    • Knowledge of Django and database design will be added advantage.
    • Strong analytical and problemsolving skills.

    Responsibilities :

    • Model Deployment : Deploy ML models into production environments, ensuring reliability, scalability, and performance.
    • Infrastructure Management : Set up and manage infrastructure for ML model deployment, including cloud services, containerization platforms, and orchestration tools.
    • Continuous Integration and Deployment (CI/CD) : Implement CI/CD pipelines for ML workflows, enabling automated testing, validation, and deployment of models.
    • Monitoring and Logging : Develop monitoring and logging solutions to track the performance of deployed models, detect anomalies, and troubleshoot issues.
    • Model Versioning and Governance : Establish processes for versioning ML models, tracking changes, and ensuring compliance with regulatory requirements.
    • Scalability and Efficiency : Optimize ML model performance and resource utilization through techniques such as model serving optimization, batch processing, and distributed computing.
    • Collaboration : Collaborate with data scientists, software engineers, and DevOps teams to streamline the ML development lifecycle and improve crossfunctional workflows.
    • Documentation and Knowledge Sharing : Document deployment processes, best practices, and troubleshooting guidelines to facilitate knowledge sharing and enable team members to work effectively
    )
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