Senior MLOps Engineer

Job Title: Cloud and MLOps Developer

Location: Bangalore/Gurgaon

Job Summary:

We are seeking a skilled Cloud and MLOps Developer to design, implement, and manage cloud infrastructure and MLOps pipelines that support the deployment, monitoring, and automation of machine learning models at scale. You will collaborate with data scientists, ML engineers, and DevOps teams to ensure reliable and secure delivery of AI/ML solutions using modern cloud platforms and CI/CD practices.

Key Responsibilities:

• Develop and manage cloud-native infrastructure (e.g., using AWS, Azure, or GCP) for machine learning workflows.
• Design and implement end-to-end MLOps pipelines for model training, validation, deployment, and monitoring.
• Automate infrastructure provisioning and deployment using IaC tools like Terraform or ARM templates.
• Containerize ML applications using Docker and orchestrate with Kubernetes or other orchestration tools.
• Set up and manage CI/CD pipelines using tools like GitHub Actions, Azure DevOps, Jenkins, or GitLab CI.
• Collaborate with data scientists to optimize model deployment, monitoring, and retraining strategies.
• Ensure security, compliance, and scalability of deployed ML models and data pipelines.
• Implement logging, monitoring, and alerting systems using tools like Prometheus, Grafana, or Azure Monitor.

Required Skills:

• Hands-on experience with cloud platforms (AWS, Azure, or GCP).
• Proficiency in MLOps tools like MLflow, Kubeflow, or SageMaker Pipelines.
• Strong knowledge of Docker, Kubernetes, and container orchestration.
• Proficient in Python, Bash, and basic scripting for automation.
• Experience with CI/CD tools and version control systems (e.g., Git).
• Familiarity with data and model versioning tools like DVC.
• Good understanding of machine learning lifecycle and ML model management.
• Knowledge of networking, security, and infrastructure automation.

Preferred Qualifications:

• Certifications in cloud platforms (e.g., Azure DevOps Engineer, AWS Solutions Architect).
• Experience working in Agile/Scrum environments.
• Familiarity with monitoring and observability in ML systems.
• Experience integrating with APIs and microservices in a production environment.

Education & Experience:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 8+ years of experience in DevOps, Cloud, or MLOps roles.