Key Responsibilities

• AI Model Deployment & Monitoring – Automate the deployment of AI/ML models in cloud and on-premise environments using MLOps practices.
• CI/CD Pipeline Implementation – Design and maintain continuous integration and deployment (CI/CD) pipelines for AI applications.
• Infrastructure Automation – Use tools like Terraform, Ansible, or Kubernetes to manage AI infrastructure efficiently.
• Cloud & Container Management – Deploy AI models on AWS, Azure, GCP, or on-premise using Docker, Kubernetes, and serverless architectures.
• Performance Optimization & Scaling – Monitor and optimize AI models, ensuring they run efficiently under high workloads.
• Security & Compliance – Implement best practices for AI model security, data privacy, and compliance with industry standards.
• Collaboration with Data Scientists & Engineers – Work closely with AI researchers, data engineers, and software developers to streamline AI operations.

Required Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Science, IT, or a related field
• 3+ years of experience in DevOps, MLOps, or AI deployment
• Proficiency in Python, Bash, or Golang for automation
• Experience with Docker, Kubernetes, and CI/CD tools (Jenkins, GitHub Actions, GitLab CI/CD)
• Expertise in cloud platforms (AWS, GCP, Azure) and infrastructure as code (Terraform, Ansible)
• Familiarity with AI/ML frameworks (TensorFlow, PyTorch, MLflow, Kubeflow)
• Knowledge of monitoring tools (Prometheus, Grafana, ELK Stack)

Preferred Qualifications

• Certifications in AWS Certified DevOps Engineer, Google Cloud DevOps, or Kubernetes
• Experience with AI model serving tools like TensorFlow Serving, TorchServe, or ONNX
• Understanding of security best practices in AI model deployment

Job Benefits

• Competitive salary & performance-based bonuses
• Opportunity to work with cutting-edge AI & DevOps technologies
• Remote/hybrid work flexibility
• Continuous learning & growth opportunities in AI and cloud automation