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3 days ago
We are seeking a seasoned professional to lead the deployment and management of machine learning models in production environments. This role requires a strong understanding of ML model lifecycle management, containerization, orchestration, and cloud services.
Key Responsibilities:- Design and implement end-to-end CI/CD pipelines for ML models to streamline development and deployment processes.
- Develop and maintain infrastructure required for model deployment, including containerization (e.g., Docker), orchestration (e.g., Kubernetes), and cloud services (e.g., AWS, Azure).
- Monitor model performance, troubleshoot issues, and perform regular maintenance to ensure models remain accurate and effective.
- Bachelor's degree in computer science, data science, statistics, or related field, or master's degree or higher.
- Total 7+ years of industry experience.
- More than 3 years of experience in ML Ops, DevOps, or related role, with solid understanding of deploying and managing ML models in production environments.
- Prior experience in engineering domain.
- Prior experience in working with teams in Scaled Agile Framework (SAFe).
You will be responsible for overseeing the entire lifecycle of machine learning models, from deployment to monitoring and maintenance. You will work closely with data scientists, ML engineers, and development teams to ensure that ML models are effectively integrated into production systems and deliver high performance.
Your Skills- Strong understanding of ML model lifecycle management.
- Experience with containerization technologies (e.g., Docker) and orchestration platforms (e.g., Kubernetes).
- Familiarity with cloud services (e.g., AWS, Azure) and their ML offerings.
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