Senior Cloud Engineer (5-8 yrs )

About the Role

We are looking for a Senior Cloud Engineer with strong hands-on experience in Azure, Databricks, Kubernetes, MLOps and cloud automation to support ML development teams and drive end-to-end automation for model deployment and operations.

The role will also focus on enterprise-scale Agentic AI systems, including evaluating, prototyping and recommending AI platforms for production adoption.

Experience: 5–8 Years

Location: Bangalore

Employment Type: Full-time

Key Responsibilities

• Build and maintain CI/CD/CT pipelines for ML models using Azure DevOps, GitHub Actions, or Jenkins.
• Develop and manage deployment workflows for Databricks Jobs, MLflow models, and microservices running on AKS/ARO.
• Automate cloud infrastructure using Terraform, scripting, and GitOps practices.
• Manage and optimize Azure, Databricks workspaces, AKS/ARO clusters, networking, and model-serving environments.
• Implement monitoring, logging, alerting, and reliability practices for ML systems and cloud platforms.
• Support the deployment and operationalization of LLM-based and Agentic AI systems.
• Work with RAG pipelines, agent workflows, and enterprise AI applications.
• Evaluate and prototype AI platforms including Azure AI Foundry, AWS Bedrock/AgentCore, Google Cloud Gemini, Databricks AgentBricks, and TrueFoundry.
• Collaborate closely with ML Engineers, Data Engineers, and Application teams.
• Ensure strong practices around security, governance, scalability, reliability, and cloud cost optimization.

Required Skills

• 5–8 years of experience in Cloud Engineering / MLOps / Platform Engineering.
• Strong hands-on experience with Microsoft Azure, AKS, ARO, and Databricks.
• Experience with MLflow and Kubernetes-based model deployments.
• Strong knowledge of CI/CD, infrastructure automation, and GitOps.
• Proficiency in Python and Bash/PowerShell scripting.
• Strong understanding of cloud networking, security, distributed systems, and platform reliability.
• Experience working with LLM-based systems, Agentic AI, RAG pipelines, or enterprise AI applications.
• Experience with Terraform and cloud infrastructure automation

Preferred / Good to Have

• Exposure to Azure AI Foundry, AWS Bedrock/AgentCore, Google Cloud Gemini, Databricks AgentBricks, or TrueFoundry.
• Experience evaluating and selecting platforms for production AI/ML workloads.
• Experience working with enterprise-scale AI or MLOps platforms.
• Knowledge of model monitoring, observability, and AI governance

Tech Stack

Azure | Databricks | AKS | ARO | Terraform | MLflow | Kubernetes | CI/CD | GitOps | Python | Bash/PowerShell | Agentic AI | LLM | RAG

If you are passionate about building scalable cloud infrastructure and enabling production-grade AI/ML systems, we'd love to hear from you.