Senior ML Ops Engineer

• Design, develop, and maintain scalable backend systems and APIs using Python and cloud-native technologies.
• Build, deploy, and optimize machine learning models, training pipelines, and ML-powered applications in production environments.
• Develop, maintain, and improve CI/CD pipelines across multiple platforms using tools such as Jenkins, GitHub Actions, and related automation frameworks.
• Containerize applications and manage deployments using Docker and Kubernetes to ensure high availability and scalability
• Implement Infrastructure as Code (IaC) practices using tools such as Terraform, CloudFormation, or equivalent solutions.
• Collaborate with data scientists, software engineers, and product teams to integrate ML models into production systems.
• Design and manage cloud infrastructure on AWS and other cloud platforms to support ML workloads and backend services. [
• Monitor application performance, availability, and reliability using tools such as Prometheus, Grafana, and ELK Stack.
• Implement security best practices, access controls, and compliance standards across applications and infrastructure.
• Troubleshoot complex production issues, perform root cause analysis, and drive continuous improvements.
• Conduct system testing for performance, scalability, security, and availability requirements.
• Automate operational processes, deployment workflows, and infrastructure management using Python, Shell, and Groovy scripting.
• Work closely with development teams to establish and enhance deployment strategies and release management processes.
• Maintain documentation for architecture, deployment procedures, monitoring, and operational practices.
• Participate in code reviews, architectural discussions, and technical decision-making.
• Mentor junior engineers and contribute to engineering excellence and best practices.
• Independently drive projects and collaborate effectively with cross-functional teams to deliver business objective.

Preferred candidate profile

• 8+ years of experience in Backend Engineering, MLOps, DevOps, or Cloud Engineering.
• Strong expertise in Python, REST APIs, AWS, Docker, and Kubernetes.
• Hands-on experience with ML frameworks such as PyTorch, TensorFlow, and Scikit-learn.
• Proven experience building ML training, deployment, and monitoring pipelines.
• Strong knowledge of CI/CD automation, Infrastructure as Code, and cloud-native architectures.
• Excellent problem-solving, communication, stakeholder management, and teamwork skill