Azure AI Engineer & MLOps Lead

GSPANN Technologies, Inc

Fremont (CA)

On-site

USD 180,000 - 240,000

Full time

4 days ago

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Job summary

GSPANN Technologies, Inc. in Fremont, CA seeks an experienced Azure AI Engineer / End-to-End Engineering Lead to design, develop, deploy, and operate production-grade AI/ML solutions on Microsoft Azure.

The role emphasizes Azure AI, DevOps, MLOps, and cloud engineering with enterprise-scale delivery. The ideal candidate will guide the complete lifecycle from development and infrastructure automation to deployment, monitoring, and production operations, collaborating with data scientists and

Qualifications

12–15+ years of overall technology experience.
Hands-on experience designing AI/ML solutions on Microsoft Azure.
Experience with MLOps, DevOps, CI/CD, and enterprise AI/ML deployment strategies.
Hands-on with Azure DevOps Pipelines and/or GitHub Actions.
Strong knowledge of Docker, Kubernetes, and AKS.
IaC experience with Terraform and/or Bicep.
Experience with Azure observability tools: Monitor, Application Insights, Log Analytics.
Experience securing cloud apps with Azure Key Vault and Entra ID.
Experience with Azure Data Lake Gen2, Data Factory, Databricks, Synapse, or similar.
Ability to work in PST hours; Fremont, CA local candidates preferred.

Responsibilities

Design, develop, deploy, and operate production-grade AI/ML solutions on Microsoft Azure.
Design and implement end-to-end MLOps pipelines using Azure Machine Learning.
Design and implement CI/CD pipelines for AI/ML apps and infrastructure.
Build deployment pipelines using Azure DevOps Pipelines and/or GitHub Actions.
Manage separate deployment strategies across Dev, Test, UAT, Staging, and Production environments.
Develop infrastructure with IaC using Terraform and/or Bicep.
Build and deploy containerized AI/ML apps with Docker and Kubernetes/AKS.
Integrate AI/ML with Azure Data Lake Gen2, Data Factory, Databricks, Synapse.
Implement observability with Azure Monitor, App Insights, Log Analytics.
Secure AI/ML environments using Azure Key Vault and Entra ID.
Collaborate with data scientists, engineers, and stakeholders to deliver scalable solutions.
Provide technical leadership across the end-to-end AI lifecycle.
Establish best practices for AI/ML development, MLOps, DevOps, security, and reliability.