AI DevOps Engineer

Job Summary

We are seeking a highly skilled AI DevOps Engineer to support and enhance AI/ML platform operations, cloud infrastructure automation, CI/CD pipelines, and MLOps practices for MSIG. The ideal candidate will have strong expertise in DevOps, cloud platforms, containerization, infrastructure automation, and AI/ML deployment pipelines. This role will collaborate closely with Data Scientists, ML Engineers, Software Developers, and Infrastructure teams to operationalize scalable AI solutions.

Key Responsibilities

AI/ML Platform & MLOps

• Design, implement, and maintain scalable AI/ML infrastructure and MLOps pipelines.
• Automate model deployment, retraining, monitoring, and versioning processes.
• Manage end-to-end ML lifecycle including model packaging, deployment, and production support.
• Integrate ML workflows with CI/CD pipelines for seamless deployment.
• Support model governance, monitoring, drift detection, and rollback mechanisms.

DevOps & Cloud Engineering

• Build and manage CI/CD pipelines using Jenkins, GitHub Actions, GitLab CI/CD, or Azure DevOps.
• Automate infrastructure provisioning using Terraform, CloudFormation, or ARM templates.
• Manage Kubernetes clusters and containerized applications using Docker and Kubernetes/OpenShift/EKS/AKS/GKE.
• Implement Infrastructure as Code (IaC) and configuration management best practices.
• Ensure high availability, scalability, and reliability of AI applications.

Cloud & Infrastructure

• Work with cloud platforms such as AWS, Azure, or GCP.
• Configure and maintain cloud-native AI services and compute resources.
• Implement monitoring, logging, and alerting using tools such as Prometheus, Grafana, ELK, Datadog, or CloudWatch.
• Optimize infrastructure performance and cloud costs.

Security & Compliance

• Implement DevSecOps best practices for AI environments.
• Ensure compliance with enterprise security standards and regulatory requirements.
• Manage IAM, secrets management, vulnerability scanning, and container security.

Collaboration & Support

• Collaborate with AI/ML teams to productionize machine learning models.
• Troubleshoot deployment and infrastructure issues across environments.
• Participate in architecture discussions and operational planning.
• Provide production support and incident resolution.

Required Skills

Technical Skills

• Strong experience with DevOps and MLOps practices.
• Expertise in:
  • Docker
  • Kubernetes/OpenShift
  • Jenkins / GitHub Actions / GitLab CI
  • Terraform / IaC tools
  • Linux Administration
  • Python or Shell scripting
• Experience with AI/ML deployment frameworks:
  • MLflow
  • Kubeflow
  • SageMaker
  • Vertex AI
  • Azure ML
• Cloud experience in AWS, Azure, or GCP.
• Experience with monitoring/logging tools:
  • Prometheus
  • Grafana
  • ELK Stack
  • Splunk
• Knowledge of networking, security, and cloud architecture.

AI/ML Knowledge

• Understanding of machine learning workflows and model lifecycle.
• Experience deploying AI/ML models into production environments.
• Familiarity with LLMOps / Generative AI deployment is a plus.
• Exposure to vector databases, GPU workloads, and AI inferencing platforms preferred.

Preferred Qualifications

• Experience in Insurance or Financial Services domain.
• Knowledge of Data Engineering pipelines and streaming platforms like Kafka.
• Experience with GPU infrastructure and AI acceleration platforms.
• Familiarity with Responsible AI and AI governance frameworks.
• Relevant certifications in AWS/Azure/GCP or Kubernetes preferred.