1.MlOps
JD:
Key Responsibilities:
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Maintain and support machine learning applications running on Windows and Linux servers in on-premises environments.
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Manage and troubleshoot Kubernetes clusters hosting ML workloads.
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Collaborate with data scientists and engineers to deploy machine learning models reliably and efficiently.
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Implement and maintain monitoring and ing solutions using DataDog to ensure system health and performance.
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Debug and resolve issues in production environments using Python and monitoring tools.
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Automate operational tasks to improve system reliability and scalability.
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Ensure best practices in security, performance, and availability for ML applications.
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Document system architecture, deployment processes, and troubleshooting guides.
Required Qualifications:
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Proven experience working with Windows and Linux operating systems in production environments.
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Hands-on experience managing on-premises servers and Kubernetes clusters and Docker containers.
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Strong proficiency in Python programming, with a solid background in developing Python-based solutions and applications.
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Deep understanding of machine learning workflows, including practical knowledge of ML model training processes, frameworks, and evaluation.
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Proven experience in ML deployment development, including building out end-to-end model serving pipelines and managing the model lifecycle.
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Familiarity with monitoring and debugging tools, e.g., DataDog.
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Ability to troubleshoot complex issues in distributed systems.
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Experience with CI/CD pipelines for ML applications.
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Familiarity with AWS cloud platforms.
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Background in Site Reliability Engineering or DevOps practices.
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Strong problem-solving skills and attention to detail.
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Excellent communication and collaboration skills.
AI, AWS, Data Science, Jenkins, ML, Python
