Full Job Description
Job Title: Dev Ops Engineer
Location: Atlanta, GA
Job Description:
The primary responsibility of this role is to design, implement, deploy, and support systematic cloud and AI-agent solutions that align with the organization's technical, security, operational, and management standards. The Cloud DevOps Engineer will own reliable deployment patterns for AI agents and Model Context Protocol (MCP) servers, troubleshoot complex platform issues, and apply DevOps principles and best practices.
This position provides operational support for Microsoft Azure and Google Cloud Platform (GCP) environments. Responsibilities include day-to-day incident resolution, service restoration, platform administration, and the secure deployment of cloud infrastructure, AI agents, MCP servers, APIs, and supporting services through automated CI/CD pipelines.
The role requires strong collaboration and communication skills. The successful candidate will work within a global organization, partner with colleagues across multiple locations and cultures, and communicate effectively with team members and stakeholders who have varying levels of technical expertise. A strong aptitude for learning and applying new technologies is essential.
Key Areas of Responsibility:
The team is responsible for meeting documented service-level agreements and defined platform availability targets within a 24x7x365 operating model. Key responsibilities include:
• Diagnose and resolve technical, operational, and software issues affecting installed systems and cloud services.
• Implement, administer, and support Microsoft Azure services, including Virtual Machines, Azure Data Factory, Azure Databricks, App Service, Logic Apps, Microsoft Entra ID, Azure networking, Azure Storage, security services, and identity controls such as MFA, SSO, SAML, RBAC, and Conditional Access policies.
• Implement, administer, and support Google Cloud services, including Cloud Run, BigQuery, Databricks, Customer Experience (CX) Agents, Dialogflow, Artifact Registry, Cloud Storage buckets, and Google Cloud networking services.
• Design, deploy, configure, and operate production-ready AI agents across approved cloud platforms, including versioning, environment promotion, secrets management, access controls, observability, rollback, and operational support.
• Deploy and operate Model Context Protocol (MCP) servers and integrations, including secure authentication and authorization, tool and resource configuration, network controls, logging, monitoring, vulnerability remediation, and controlled release management.
• Develop and maintain reusable CI/CD scripts and pipeline templates that automate application, agent, MCP server, infrastructure, configuration, test, security-check, and deployment workflows.
• Diagnose environmental and platform issues and coordinate with vendors when escalation or specialist support is required.
• Plan and schedule the installation of core infrastructure and associated supporting components.
• Monitor, track, analyze, and compare system performance using testing, observability, monitoring, and analysis tools.
• Respond to system alerts, assess operational impact, and reprioritize work as circumstances change.
• Create and present clear technical information for both technical and non-technical audiences.
• Contribute technical content to operational procedures and regularly review documentation for accuracy and completeness.
• Work toward defined objectives, manage priorities, and meet committed delivery dates.
• Quickly learn and apply new concepts, technologies, tools, and operational techniques.
• Collaborate constructively within the immediate team and across the broader organization.
• Communicate information clearly and persuasively in both written and verbal formats.
• Review operational data, identify recurring issues and trends, and translate findings into practical improvements to current processes.
Ideal Technical and Professional Qualifications:
• Bachelor's degree in computer science, Engineering, Computer Information Systems, or an equivalent combination of education and relevant experience.
• At least one to two years of relevant experience in cloud administration, DevOps, platform operations, infrastructure support, or a related field.
• Azure and/or Google Cloud administration certifications are preferred but not required.
• Strong hands-on scripting and automation skills using PowerShell, Bash, Python, Azure CLI, Google Cloud CLI, and Databricks CLI are required.
• Strong hands-on experience building, troubleshooting, and maintaining CI/CD pipelines using Azure DevOps and GitHub Actions is required, including scripted build, test, security validation, deployment, rollback, and environment-promotion workflows.
• Demonstrated experience deploying AI agents into controlled development, test, and production environments is required.
• Hands-on experience deploying, securing, integrating, and supporting MCP servers is required.
• Experience with Infrastructure as Code technologies such as Terraform, ARM templates, and Bicep.
• Knowledge of monitoring and management platforms such as Azure Monitor, Log Analytics, Google Cloud Logs Explorer, and Splunk.
Offers of employment are conditional upon passage of screening criteria applicable to the job
