MLOps / AI Infrastructure Engineer

📋 Description

• Architect, build, and maintain scalable model deployment and serving pipelines for LLMs, deep
• Develop production-grade model-serving architectures using technologies such as Triton, Ray
• Build and automate CI/CD pipelines covering data ingestion, feature management, model training
• Design and maintain reliable ML infrastructure capable of supporting large-scale AI and generative
• Manage containerized workloads and orchestration platforms, including Kubernetes, across cloud
• Monitor GPU and CPU cluster utilization and identify opportunities to improve performance

🎯 Requirements

• 4–8 years of professional experience in cloud infrastructure, DevOps, backend engineering, MLOps
• Strong proficiency in Python and hands-on experience with Docker, Kubernetes, and Terraform.
• Practical experience working with cloud platforms and managed AI/ML services such as AWS Bedrock
• Solid understanding of machine learning infrastructure, model deployment, model serving, and
• Hands-on familiarity with vector databases such as Pinecone, Milvus, Qdrant, or similar
• Experience with LLM orchestration frameworks and modern generative AI infrastructure.

🎁 Benefits

• Annual compensation of INR 1,800,000–2,500,000.
• Full-time employment opportunity.
• Remote working arrangement with flexibility to work remotely from India.
• Opportunity to work on large-scale machine learning and generative AI infrastructure.
• Exposure to modern AI/ML technologies, cloud platforms, GPU infrastructure, and model-serving
• Significant technical ownership across deployment, automation, scalability, and infrastructure