MLOps Engineer

2 months ago

Bengaluru, India Change leaders Full time

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 problem-solving 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 cross-functional workflows.

- Documentation and Knowledge Sharing : Document deployment processes, best practices, and troubleshooting guidelines to facilitate knowledge sharing and enable team members to work effectively

(ref:hirist.tech)

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