CDIKoregaon Park, Pune, India
Azure AI Cloud Engineer - SG
Hybride
Project context
As the Azure Cloud Engineer (with GCP exposure), you will be responsible for designing, implementing and optimizing scalable, resilient cloud infrastructure of Azure platforms. This role involves deploying, automating, and maintaining cloud-based applications, services and tools – which increase focus on AI and Data platform infrastructure- to ensure high availability, security and performance. The ideal candidate will have strong and depth knowledge of Azure services and architecture best practices, hands-on infrastructure-as-code expertise (especially Terraform), and a growing interest in enabling AI/ML workloads, infrastructure automation, monitoring and troubleshooting. Experience supporting AI projects and using modern AI tools to accelerate infrastructure work is highly valued.
Work Location : Koregaon Park, Pune
Work model: Hybrid working (3 days / week in office)
Work hours: CET Time zone (9 hours)
Goals and deliverables
We count on you for:
Design and implement secure, scalable and highly available cloud infrastructure using GCP/Azure services, based on business and technical requirements, with emphasis on enabling AI, Analytics, and data-intensive workloads.
Develop automated deployment pipelines using Infrastructure-as-Code (IaC) tools such as Terraform, GCP/Azure CloudFormation and GCP/Azure CDK, ensuring efficient, repeatable and consistent infrastructure deployments – including complex, multi-service AI and Data platforms
Implement and manage security practices such as Identity and Access Management, network security and encryption to ensure data protection and compliance with industry standards and regulations
Design, provision, and manage Azure AI and Data platform components via IAC, to support AI exploration, discovery, experimentation and production deployment, Key services include Microsoft Fabric (Lakehouse, Realtime analytics, data science and AI workload, Azure OpenAI service, Azure MLL/AI studio etc
Partner with data science, AI engineering, MLOps, and data teams to design and deliver governed AI Landing zones and environments that accelerate model development, agentic systems, implement backup, disaster recovery and failover solutions for high availability and business continuity
Leverage agentic AI Tools, GitHub copilot, Azure AI Agents and custom agent workflows as well as AI-assisted engineering practices to significantly boost productively in writing/reviewing Terraform/Bicep code, infrastructure troubleshooting, documentation generation, cost optimization, policy-as-code, and proactive platform improvement.
Create and maintain comprehensive documentation of infrastructure architecture, configuration and troubleshooting steps and share knowledge with team members
Close collaboration with multi-cloud enterprise architect, DevOps solution architect, Cloud Operations Manager to ensure quick MVP prior to pushing into production
Keep up to date with new GCP/Azure services, features and best practices – Especially in the AI, Data, Fabric and MLOps space, providing recommendations for process and architecture improvements
Education and experience
Bachelor’s degree in information technology, Computer Science, Business Administration, or related field. Master's degree or relevant certifications would be a plus.
Minimum of 5 years of experience in cloud engineering, cloud architecture or infrastructure role, ideally with exposure to data or AI/ML workloads.
Proven experience with GCP/Azure services, including Virtual Machines / Compute Engine, Blob Storage / Azure Data Lake Storage / Cloud Storage, Azure SQL Database / Cosmos DB or Cloud SQL, Azure Functions / Cloud Functions, Virtual Network / VPC, Microsoft Entra ID / IAM and RBAC, and native monitoring tools. Hands-on experience deploying and managing Azure AI and analytics services (Microsoft Fabric, Azure OpenAI Service, Azure Machine Learning / Azure AI Studio, Azure AI Search, and related components) via Infrastructure-as-Code (Terraform) is highly desirable
Hands-on experience with Infrastructure-as-Code (IaC) tools such as Terraform, GCP/Azure CloudFormation or GCP/Azure CDK, Bicep, and ARM templates for Azure (and equivalent tools for GCP). Ability to codify complex, governed AI/data platforms is a significant advantage.
Strong scripting skills in Python, Bash or PowerShell for automation tasks
Familiarity with CI/CD tools (eg: Gitlab CI/CD, Jenkins) and experience integrating them with GCP/Azure
Knowledge of networking fundamentals and experience with GCP/Azure VPC, security groups, VPN and routing, and secure private connectivity patterns for AI services (e.g., private access to OpenAI, Fabric, and storage).
Proficiency in monitoring and logging tools such as native cloud tools or third-party tools like Datadog and Splunk
Familiarity with or hands-on use of agentic AI systems, AI coding assistants, or multi-agent frameworks to augment infrastructure-as-code development, operational automation, and cloud engineering workflows is a distinct plus. Examples include GitHub Copilot (or similar) for Terraform/Bicep, custom agents for infra tasks, Azure AI Agent Service, or equivalent.
Cybersecurity Expertise:
Understanding of cybersecurity principles, best practices, and frameworks.
Knowledge of encryption, identity management, access controls, and other security measures within cloud environments.
Preferably with certifications such as GCP/Azure Certified DevOps Engineer, GCP/Azure Certified Sys Ops Administrator, GCP/Azure Certified Solutions Architect
Skills and behavioral competencies:
Excellent problem solving and troubleshooting abilities
Result orientation, influence & impact
Empowerment & accountability with the ability to work independently
Team spirit, building relationships, collective accountability
Excellent oral and written communication skills for documenting and sharing information with technical and non-technical stakeholders
Informations de la mission :
As the Azure Cloud Engineer (with GCP exposure), you will be responsible for designing, implementing and optimizing scalable, resilient cloud infrastructure of Azure platforms. This role involves deploying, automating, and maintaining cloud-based applications, services and tools – which increase focus on AI and Data platform infrastructure- to ensure high availability, security and performance. The ideal candidate will have strong and depth knowledge of Azure services and architecture best practices, hands-on infrastructure-as-code expertise (especially Terraform), and a growing interest in enabling AI/ML workloads, infrastructure automation, monitoring and troubleshooting. Experience supporting AI projects and using modern AI tools to accelerate infrastructure work is highly valued.
Work Location : Koregaon Park, Pune
Work model: Hybrid working (3 days / week in office)
Work hours: CET Time zone (9 hours)
Goals and deliverables
We count on you for:
Design and implement secure, scalable and highly available cloud infrastructure using GCP/Azure services, based on business and technical requirements, with emphasis on enabling AI, Analytics, and data-intensive workloads.
Develop automated deployment pipelines using Infrastructure-as-Code (IaC) tools such as Terraform, GCP/Azure CloudFormation and GCP/Azure CDK, ensuring efficient, repeatable and consistent infrastructure deployments – including complex, multi-service AI and Data platforms
Implement and manage security practices such as Identity and Access Management, network security and encryption to ensure data protection and compliance with industry standards and regulations
Design, provision, and manage Azure AI and Data platform components via IAC, to support AI exploration, discovery, experimentation and production deployment, Key services include Microsoft Fabric (Lakehouse, Realtime analytics, data science and AI workload, Azure OpenAI service, Azure MLL/AI studio etc
Partner with data science, AI engineering, MLOps, and data teams to design and deliver governed AI Landing zones and environments that accelerate model development, agentic systems, implement backup, disaster recovery and failover solutions for high availability and business continuity
Leverage agentic AI Tools, GitHub copilot, Azure AI Agents and custom agent workflows as well as AI-assisted engineering practices to significantly boost productively in writing/reviewing Terraform/Bicep code, infrastructure troubleshooting, documentation generation, cost optimization, policy-as-code, and proactive platform improvement.
Create and maintain comprehensive documentation of infrastructure architecture, configuration and troubleshooting steps and share knowledge with team members
Close collaboration with multi-cloud enterprise architect, DevOps solution architect, Cloud Operations Manager to ensure quick MVP prior to pushing into production
Keep up to date with new GCP/Azure services, features and best practices – Especially in the AI, Data, Fabric and MLOps space, providing recommendations for process and architecture improvements
Education and experience
Bachelor’s degree in information technology, Computer Science, Business Administration, or related field. Master's degree or relevant certifications would be a plus.
Minimum of 5 years of experience in cloud engineering, cloud architecture or infrastructure role, ideally with exposure to data or AI/ML workloads.
Proven experience with GCP/Azure services, including Virtual Machines / Compute Engine, Blob Storage / Azure Data Lake Storage / Cloud Storage, Azure SQL Database / Cosmos DB or Cloud SQL, Azure Functions / Cloud Functions, Virtual Network / VPC, Microsoft Entra ID / IAM and RBAC, and native monitoring tools. Hands-on experience deploying and managing Azure AI and analytics services (Microsoft Fabric, Azure OpenAI Service, Azure Machine Learning / Azure AI Studio, Azure AI Search, and related components) via Infrastructure-as-Code (Terraform) is highly desirable
Hands-on experience with Infrastructure-as-Code (IaC) tools such as Terraform, GCP/Azure CloudFormation or GCP/Azure CDK, Bicep, and ARM templates for Azure (and equivalent tools for GCP). Ability to codify complex, governed AI/data platforms is a significant advantage.
Strong scripting skills in Python, Bash or PowerShell for automation tasks
Familiarity with CI/CD tools (eg: Gitlab CI/CD, Jenkins) and experience integrating them with GCP/Azure
Knowledge of networking fundamentals and experience with GCP/Azure VPC, security groups, VPN and routing, and secure private connectivity patterns for AI services (e.g., private access to OpenAI, Fabric, and storage).
Proficiency in monitoring and logging tools such as native cloud tools or third-party tools like Datadog and Splunk
Familiarity with or hands-on use of agentic AI systems, AI coding assistants, or multi-agent frameworks to augment infrastructure-as-code development, operational automation, and cloud engineering workflows is a distinct plus. Examples include GitHub Copilot (or similar) for Terraform/Bicep, custom agents for infra tasks, Azure AI Agent Service, or equivalent.
Cybersecurity Expertise:
Understanding of cybersecurity principles, best practices, and frameworks.
Knowledge of encryption, identity management, access controls, and other security measures within cloud environments.
Preferably with certifications such as GCP/Azure Certified DevOps Engineer, GCP/Azure Certified Sys Ops Administrator, GCP/Azure Certified Solutions Architect
Skills and behavioral competencies:
Excellent problem solving and troubleshooting abilities
Result orientation, influence & impact
Empowerment & accountability with the ability to work independently
Team spirit, building relationships, collective accountability
Excellent oral and written communication skills for documenting and sharing information with technical and non-technical stakeholders
Informations de la mission :
- Début de mission : 09/07/2026
- Durée : 12 mois
- Date de publication : 08/10/2026
- Date limite de candidature : 30/10/2026
Compétences recherchées
Azure (Indispensable)GCP (Indispensable)Terraform (Indispensable)Bicep (Indispensable)ARM templates (Indispensable)CloudFormation (Indispensable)CDK (Indispensable)Microsoft Fabric (Indispensable)Azure OpenAI Service (Indispensable)Azure Machine Learning (Indispensable)Azure AI Studio (Indispensable)Azure AI Agents (Indispensable)GitHub Copilot (Indispensable)Python (Indispensable)Bash (Indispensable)PowerShell (Indispensable)GitLab CI/CD (Indispensable)Jenkins (Indispensable)Datadog (Indispensable)Splunk (Indispensable)Virtual Machines (Indispensable)Virtual Network (Indispensable)VPC (Indispensable)IAM (Indispensable)RBAC (Indispensable)Azure SQL (Indispensable)Cosmos DB (Indispensable)Blob Storage (Indispensable)Azure Data Lake Storage (Indispensable)Azure Functions (Indispensable)MLOps (Indispensable)
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