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.

Work arrangement
No