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