Bengaluru, Karnataka
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

- Solid 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

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📌 Senior Technical Lead (India)
🏢 HCLTech
📍 India