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Enabling Areas - ML Ops - AM - Information Technology
2-7 years
Mumbai
1 vacancy
Enabling Areas - ML Ops - AM - Information Technology
Deloitte
posted 15d ago
Job Role Insights
Flexible timing
Key skills for the job
Job Description
- Design, implement, and maintain end-to-end ML pipelines for deploying and monitoring models in production.
- Collaborate with data scientists to transition models from development to production, ensuring reproducibility and efficiency.
- Automate processes for model training, validation, and deployment using CI/CD practices.
- Monitor model performance and implement retraining strategies as needed.
- Manage cloud infrastructure and resources for machine learning workloads, ensuring cost efficiency and scalability.
- Develop and maintain documentation for ML processes, architectures, and best practices.
- Troubleshoot and resolve issues related to model deployment and operational performance.
- Stay informed about the latest trends and advancements in ML Ops and related technologies.
- Bachelor s or Master s degree in Computer Science, Data Engineering, Machine Learning, or a related field.
- Minimum of 4+ years experience with ML Ops tools and frameworks (e.g., MLflow, Kubeflow, TensorFlow Extended).
- Proficiency in programming languages such as Python and experience with machine learning libraries (e.g., TensorFlow, PyTorch).
- Experience with containerization and orchestration tools (e.g., Docker, Kubernetes).
- Familiarity with cloud platforms (e.g., AWS, GCP, Azure) for deploying ML solutions.
- Knowledge of version control systems (e.g., Git, Azure Repos) and CI/CD methodologies.
- Strong problem-solving skills and ability to work collaboratively in a fast-paced environment.
- Experience deploying and managing machine learning models in production environments.
- Knowledge of monitoring and logging tools.
- Familiarity with data engineering concepts and tools (e.g., Apache Airflow, Apache Spark).
Employment Type: Full Time, Permanent
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