MLOps Engineer

Job Summary:

We are looking for an experienced MLOps Engineer with 8+ years of experience in backend engineering and machine learning operations. The ideal candidate will have strong expertise in Python, cloud infrastructure, CI/CD, containerization, and ML model deployment. You will be responsible for building and maintaining scalable ML pipelines, automating deployment workflows, managing cloud-native infrastructure, and collaborating with cross-functional teams to deliver reliable, secure, and high-performing machine learning solutions in production.

Key Responsibilities:

• Design, develop, and maintain scalable backend services using Python, APIs, and cloud infrastructure.
• Build, deploy, and optimize machine learning models, training pipelines, and ML-powered applications.
• Develop, maintain, and enhance CI/CD pipelines for applications across multiple platforms.
• Collaborate with development, data science, and engineering teams to streamline deployment processes and release strategies.
• Containerize and deploy applications using Docker and Kubernetes.
• Manage cloud infrastructure and ensure secure, scalable, and highly available environments.
• Implement Infrastructure as Code (IaC) for automated infrastructure provisioning and management.
• Monitor system performance, availability, and reliability using monitoring and logging tools.
• Ensure system security, stability, and automated recovery mechanisms.
• Conduct performance, security, and availability testing across environments.
• Troubleshoot production issues and optimize deployment workflows.
• Work independently while effectively collaborating with cross-functional engineering teams.
• Document deployment processes, infrastructure configurations, and operational procedures.

Required Skills:

• 8+ years of experience in Backend Engineering and MLOps.
• Strong programming experience in Python.
• Hands-on experience with Machine Learning, including model development, training pipelines, and ML feature implementation.
• Experience with ML frameworks such as PyTorch, TensorFlow, and Scikit-learn.
• Strong expertise in developing REST APIs and backend applications.
• Hands-on experience with CI/CD tools such as Jenkins and GitHub CI/GitHub Actions.
• Experience with Docker and Kubernetes for containerization and orchestration.
• Strong knowledge of AWS cloud services and infrastructure.
• Experience implementing Infrastructure as Code (IaC) solutions.
• Hands-on experience with monitoring and logging tools such as Prometheus, Grafana, and the ELK Stack.
• Strong scripting skills in Python, Shell, and Groovy.
• Excellent analytical, troubleshooting, and problem-solving skills.
• Strong communication, collaboration, and organizational abilities.