GyanSys - Senior Cloud Engineer - MLOps

About GyanSys Senior Cloud Engineer - Databricks : GyanSys is a global mid-tier systems integrator with over 20 years of experience, headquartered in the United States. With 3,000+ employees across 12 countries, the company serves 160+ active enterprise customers across manufacturing, industrial, consumer, life sciences, and high-tech industries.

GyanSys delivers digital transformation services leveraging SAP, Salesforce, Databricks, Snowflake, application modernization, and AI technologies.

Job Description We are looking for a Senior Cloud Engineer - Databricks / MLOps with strong hands-on experience in Microsoft Azure, Databricks, Kubernetes, and MLOps. The ideal candidate will support machine learning development teams by building scalable cloud infrastructure and automating end-to-end model deployment, integration, and operational workflows.

Key Responsibilities

• Build and maintain CI/CD and CT pipelines for machine learning models using Azure DevOps, GitHub Actions, or Jenkins.
• Develop deployment workflows for Databricks Jobs, MLflow models, and microservices.
• Deploy, manage, and optimize workloads on Azure Kubernetes Service (AKS) and Azure Red Hat OpenShift (ARO).
• Automate cloud infrastructure provisioning and management using Terraform, scripting, and GitOps practices.
• Manage and optimize Azure Databricks workspaces and Kubernetes clusters.
• Configure networking and model-serving environments for scalable ML workloads.
• Implement monitoring, logging, and alerting for cloud infrastructure and MLOps platforms.
• Collaborate with ML Engineers, Data Engineers, and Application Development teams to streamline model deployment and operations.
• Ensure cloud security, governance, performance, and cost optimization across MLOps pipelines.

Required Skills

• 5 - 8 years of experience in cloud engineering, DevOps, platform engineering, or related roles.
• Strong hands-on experience with Microsoft Azure, AKS, ARO, and Databricks.
• Practical experience with MLflow and Kubernetes-based model deployments.
• Strong understanding of MLOps, model deployment, and ML platform operations.
• Proficiency in Python and Bash or PowerShell scripting.
• Experience with Terraform, GitOps, and CI/CD automation.
• Good understanding of cloud security, networking, and distributed systems.
• Ability to collaborate effectively with cross-functional engineering teams.

(ref:hirist.tech)