Job Summary
1) Focused on building and evaluating enterprise-scale agentic AI systems.
2) The candidate would assess, prototype, and recommend platforms including Azure AI Foundry, AWS Bedrock/AgentCore, Google Cloud Gemini, Databricks AgentBricks, and TrueFoundry for production adoption.
3) Experience with LLM-based systems — agent workflows, RAG pipelines, and enterprise AI applications Experience Required: 5–8 years
Tech Stack: Azure | Databricks | AKS | ARO | Terraform | MLflow | CI/CD
Role Summary
We are looking for a seasoned cloud engineer to support ML development teams and drive end to end automation for model deployment and operations across Azure, Databricks, and Kubernetes platforms.
Key Responsibilities
Key Responsibilities
• Build and maintain CI/CD/CT pipelines for ML models using Azure DevOps / GitHub Actions/Jenkins
• Develop and manage deployment workflows for:
o Databricks Jobs
o MLflow models
o Microservices running on AKS / ARO
• Automate infrastructure using Terraform, scripting, and GitOps practices
• Manage and optimize:
o Databricks workspaces
o AKS clusters
o Networking and model serving environments
• Implement monitoring, logging, and alerting for ML systems and platform reliability
• Collaborate closely with ML engineers, data engineers, and application teams
• Ensure security best practices, governance, and cost optimization across MLOps pipelines
Required Skills
• Strong hands on experience with Azure, AKS, ARO and Databricks
• Solid experience with MLflow, Kubernetes based model deployments
• Proficiency in Python and Bash / PowerShell
• Good understanding of cloud security, networking, and distributed systems
Skill Requirements
Key Responsibilities
• Build and maintain CI/CD/CT pipelines for ML models using Azure DevOps / GitHub Actions/Jenkins
• Develop and manage deployment workflows for:
o Databricks Jobs
o MLflow models
o Microservices running on AKS / ARO
• Automate infrastructure using Terraform, scripting, and GitOps practices
• Manage and optimize:
o Databricks workspaces
o AKS clusters
o Networking and model serving environments
• Implement monitoring, logging, and alerting for ML systems and platform reliability
• Collaborate closely with ML engineers, data engineers, and application teams
• Ensure security best practices, governance, and cost optimization across MLOps pipelines
Required Skills
• Strong hands on experience with Azure, AKS, ARO and Databricks
• Solid experience with MLflow, Kubernetes based model deployments
• Proficiency in Python and Bash / PowerShell
• Good understanding of cloud security, networking, and distributed systems