MLOps Engineer

Summary

Responsibilities

• Design and implement automated pipelines for machine learning model deployment and continuous integration.
  
• Manage cloud infrastructure and utilize specialized MLOps tools to maintain system reliability.
  
• Oversee model versioning and experiment tracking to ensure reproducibility and auditability of all data science projects.
  
• Containerize applications using Docker and orchestrate workloads with Kubernetes for scalable deployment.
  
• Diagnose and resolve production issues related to machine learning systems to minimize downtime.
  
• Develop and maintain Python-based scripts and applications to support ML workflows.
  
• Establish and enforce CI/CD practices specifically tailored for machine learning models to accelerate delivery cycles.
  

• Experience: 3-6 years of professional experience in relevant technical roles.
  
• Technical Proficiency: Strong command of Python programming and experience with Docker and Kubernetes.
  
• Domain Knowledge: Proven expertise in MLOps tools, cloud platforms, and CI/CD pipelines for machine learning.
  
• Core Skills: Demonstrated ability in automation, performance optimization, model versioning, experiment tracking, and production troubleshooting.
  
• Deployment: Hands-on experience with the end-to-end deployment of ML models in production environments.
  
• Compensation: Salary range of ₹15 LPA.