Description

You own the infrastructure and deployment lifecycle for Generative AI solutions, including LLMs and NLP platforms, integrating foundation models with enterprise applications while optimizing inference performance.

This role is on-site.

Responsibilities

• Design and implement CI/CD pipelines for AI/ML model deployment with automated testing, validation, and rollback mechanisms.
• Manage containerization and orchestration using Docker and Kubernetes, supported by Infrastructure as Code tools like Terraform.
• Deploy and manage AI workloads across Azure, AWS, or GCP, ensuring high availability, security, and low latency.
• Implement MLOps best practices, including model versioning, experiment tracking, and automated retraining pipelines.
• Monitor model and data drift, implementing remediation strategies to maintain performance and governance compliance.

Required Skills

• 8+ years of experience in DevOps, Cloud Engineering, or Platform Engineering.
• Strong proficiency in building CI/CD pipelines for both application and ML model deployments.
• Hands-on experience with cloud platforms: Azure, AWS, or GCP.
• Expertise in containerization (Docker) and orchestration (Kubernetes).
• Experience supporting ML or Generative AI workloads in production environments.
• Strong scripting skills in Python or Bash for automation.
• Understanding of Infrastructure as Code (IaC) principles.
• Experience working in Agile/DevOps delivery environments.

Preferred Skills

• Exposure to MLOps tools such as MLflow or Kubeflow.
• Understanding of AI governance, data security, and compliance frameworks.

Key Skills

Education

Any Graduate