Chapter Two Four Seven Infotech Pvt Ltd
Senior DevOps Engineer (AWS & Azure Expert)
Chapter Two Four Seven Infotech Pvt Ltd, Indore, West Virginia, United States, 25111
Job Overview:
We're looking for a proactive DevOps Engineer with strong expertise in AWS and Azure to support a wide range of projects - including MERN stack apps, Python/PHP services, data pipelines, and AI/ML deployments.
This role will focus on infrastructure automation, secure deployments, MLOps/DataOps, monitoring, and cost optimization across all environments. The engineer will also take ad-hoc sessions with the team to review current cloud setups and ensure best practices are followed.
Key Responsibilities:
Infrastructure Management: Design, implement, and manage scalable cloud infrastructure on AWS and Azure. Set up and manage CI/CD pipelines for various projects (Node.js, React, Python, PHP). Configure IaC tools like Terraform or CloudFormation/ARM templates. Application Deployment & Monitoring:
Manage deployment of MERN stack applications, REST APIs, and microservices. Manage environment setup, versioning, rollbacks, and automated testing. Include high availability, disaster recovery plans, and regular database backups to reduce downtime. Implement monitoring, logging, and alerting systems (e.g., CloudWatch, Azure Monitor, Prometheus, Grafana). Support for Data Engineering, BI, and AI/ML Projects:
Deploy and monitor data pipelines using tools such as Apache Airflow, Azure Data Factory, or AWS Glue. Support ETL workflows, ensuring efficient and secure data processing for analytics. Deploy and manage environments for data lakes, warehouses, Spark, and Kafka. Enable automated deployment of ML models using SageMaker, Azure ML Studio, or Docker. Manage MLOps workflows: model packaging, versioning, A/B testing, and monitoring. Facilitate BI platform deployments, report refresh scheduling, and access control. Apply DataOps and MLOps practices to ensure reproducibility, version control, and robust release management. Security, Cost Optimization & Compliance:
Implement cloud security best practices and ensure compliance with organizational policies. Manage secrets, certificates, access controls, and secure data handling. Maintain a solid understanding of cloud cost structures and proactively implement cost optimization strategies for compute, storage, and network services. Using tools like AWS Budgets, Azure Cost Management, or third-party CloudHealth/Spot.io for alerting and optimization. Collaboration & Documentation:
Work closely with developers, data engineers, and ML engineers to streamline deployments and resolve bottlenecks. Document infrastructure architecture, deployment steps, and recovery procedures. Key Skills & Qualifications:
Mandatory Technical Expertise:
Strong proficiency in AWS (EC2, ECS, Lambda, RDS, S3, CloudWatch, IAM, etc.). Solid experience with Azure (VMs, App Services, Azure Functions, Blob Storage, ADF, etc.). Experience with Docker, Kubernetes (EKS/AKS preferred). Proficiency in scripting with Bash, Python, or PowerShell. Project-Specific Skills:
Familiarity with Node.js, React, MongoDB deployments. Hands-on experience in deploying and scaling Python and PHP applications. Exposure to Data Engineering workflows, ETL design, and orchestration tools. Understanding of AI/ML model lifecycle, APIs, and GPU/CPU environments. Tools & Practices:
Git, GitHub/GitLab, Bitbucket Jenkins, GitHub Actions, Azure DevOps, CodePipeline Terraform, Ansible, Helm (optional) Monitoring with Prometheus/Grafana, Cloud-native monitoring tools Nice to Have:
Experience with hybrid cloud setups. Exposure to serverless frameworks. Familiarity with cloud billing dashboards and usage reports for cost control.
We're looking for a proactive DevOps Engineer with strong expertise in AWS and Azure to support a wide range of projects - including MERN stack apps, Python/PHP services, data pipelines, and AI/ML deployments.
This role will focus on infrastructure automation, secure deployments, MLOps/DataOps, monitoring, and cost optimization across all environments. The engineer will also take ad-hoc sessions with the team to review current cloud setups and ensure best practices are followed.
Key Responsibilities:
Infrastructure Management: Design, implement, and manage scalable cloud infrastructure on AWS and Azure. Set up and manage CI/CD pipelines for various projects (Node.js, React, Python, PHP). Configure IaC tools like Terraform or CloudFormation/ARM templates. Application Deployment & Monitoring:
Manage deployment of MERN stack applications, REST APIs, and microservices. Manage environment setup, versioning, rollbacks, and automated testing. Include high availability, disaster recovery plans, and regular database backups to reduce downtime. Implement monitoring, logging, and alerting systems (e.g., CloudWatch, Azure Monitor, Prometheus, Grafana). Support for Data Engineering, BI, and AI/ML Projects:
Deploy and monitor data pipelines using tools such as Apache Airflow, Azure Data Factory, or AWS Glue. Support ETL workflows, ensuring efficient and secure data processing for analytics. Deploy and manage environments for data lakes, warehouses, Spark, and Kafka. Enable automated deployment of ML models using SageMaker, Azure ML Studio, or Docker. Manage MLOps workflows: model packaging, versioning, A/B testing, and monitoring. Facilitate BI platform deployments, report refresh scheduling, and access control. Apply DataOps and MLOps practices to ensure reproducibility, version control, and robust release management. Security, Cost Optimization & Compliance:
Implement cloud security best practices and ensure compliance with organizational policies. Manage secrets, certificates, access controls, and secure data handling. Maintain a solid understanding of cloud cost structures and proactively implement cost optimization strategies for compute, storage, and network services. Using tools like AWS Budgets, Azure Cost Management, or third-party CloudHealth/Spot.io for alerting and optimization. Collaboration & Documentation:
Work closely with developers, data engineers, and ML engineers to streamline deployments and resolve bottlenecks. Document infrastructure architecture, deployment steps, and recovery procedures. Key Skills & Qualifications:
Mandatory Technical Expertise:
Strong proficiency in AWS (EC2, ECS, Lambda, RDS, S3, CloudWatch, IAM, etc.). Solid experience with Azure (VMs, App Services, Azure Functions, Blob Storage, ADF, etc.). Experience with Docker, Kubernetes (EKS/AKS preferred). Proficiency in scripting with Bash, Python, or PowerShell. Project-Specific Skills:
Familiarity with Node.js, React, MongoDB deployments. Hands-on experience in deploying and scaling Python and PHP applications. Exposure to Data Engineering workflows, ETL design, and orchestration tools. Understanding of AI/ML model lifecycle, APIs, and GPU/CPU environments. Tools & Practices:
Git, GitHub/GitLab, Bitbucket Jenkins, GitHub Actions, Azure DevOps, CodePipeline Terraform, Ansible, Helm (optional) Monitoring with Prometheus/Grafana, Cloud-native monitoring tools Nice to Have:
Experience with hybrid cloud setups. Exposure to serverless frameworks. Familiarity with cloud billing dashboards and usage reports for cost control.