Description

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

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