Principal AI Infrastructure Engineer

17 hours ago

Gandhinagar, Gujarat, India beBeeMachineLearning Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Senior MLOps Engineer

We are looking for a strategic Senior MLOps Engineer to lead the end-to-end design, implementation, and scaling of our AI infrastructure.

The ideal candidate will have strong expertise in cloud platforms, Kubernetes, Docker, Terraform, Helm, Kubeflow, and MLflow. They will also have experience with inference frameworks and proficiency in Python, TypeScript, and infrastructure-as-code.

The role involves designing and implementing scalable ML pipelines, model serving, versioning, automated CI/CD, and real-time monitoring of AI workflows. The candidate will also be responsible for building and optimizing GPU-backed serving infrastructure, driving initiatives on model governance, automated drift detection, and data-management best practices.

Additionally, the Senior MLOps Engineer will integrate vector databases for low-latency semantic retrieval, build agentic workflows using LangChain or similar frameworks, and architect RBAC-driven, isolated ML services to securely serve 100–500+ organizations.

The ideal candidate will have a Master's/PhD in CS/AI and certifications such as AWS ML Specialty, Google Cloud Professional ML Engineer, or CNCF CKA/CKAD. Prior experience at AI-focused startups or enterprises scaling ML for 100–500 orgs is preferred.

Key responsibilities include:

  • Designing and implementing scalable ML pipelines
  • Model serving, versioning, automated CI/CD, and real-time monitoring of AI workflows
  • Building and optimizing GPU-backed serving infrastructure
  • Driving initiatives on model governance, automated drift detection, and data-management best practices
  • Integrating vector databases for low-latency semantic retrieval
  • Building agentic workflows using LangChain or similar frameworks
  • Architecting RBAC-driven, isolated ML services to securely serve 100–500+ organizations

Required skills and qualifications include:

  • Strong expertise in cloud platforms, Kubernetes, Docker, Terraform, Helm, Kubeflow, and MLflow
  • Experience with inference frameworks
  • Proficiency in Python, TypeScript, and infrastructure-as-code
  • Master's/PhD in CS/AI and certifications such as AWS ML Specialty, Google Cloud Professional ML Engineer, or CNCF CKA/CKAD
  • Prior experience at AI-focused startups or enterprises scaling ML for 100–500 orgs

Benefits include:

  • Competitive salary and benefits package
  • Opportunities for professional growth and development
  • Collaborative and dynamic work environment

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