Sr Systems Engineer ; DevOps, Agentic AI

Job Summary

Performs advanced systems and infrastructure engineering functions, including planning, designing, and implementing cloud and on-premise platform solutions. This includes container orchestration (Kubernetes/Open Shift), servers, storage, networking, and backup solutions.

• Design, implement, and manage enterprise Kubernetes/Open Shift platforms with a focus on resiliency, scalability, and operational efficiency
• Define and maintain platform standards, including lifecycle management, patching, upgrades, and policy enforcement
• Engineer infrastructure and operational workflows as code, driving automation, self-healing, and scalability (using tools like Terraform, Kubernetes Operators, etc.)
• Develop, maintain, and evolve internal platforms and tools to simplify deployment, monitoring, and operational health, improving the experience for application development teams.
• Collaborate across teams (Mid Tier, Dev, Security) to deliver secure, performant, and highly-available systems.

• Design and implement CI/CD pipelines using tools such as Jenkins
• Support Git Ops deployment models leveraging tools such as ArgoCD
• Develop Infrastructure as Code (Terraform) and Configuration as Code (Ansible) to automate provisioning and management
• Automate installation, configuration, and lifecycle management of Kubernetes and supporting infrastructure
• Implement monitoring, logging, and reporting to ensure system health and performance

• Develop and maintain automation tools, services, and APIs using Java and/or Python
• Build scripts and reusable automation to improve platform reliability and developer productivity
• Collaborate with development teams to enable efficient application deployment and integration
• Apply software engineering best practices, including modular design, testing, and version control

• Leverage AI-assisted development tools to accelerate engineering workflows and improve productivity
• Apply prompt engineering techniques to create effective, repeatable interactions with AI tools for automation and problem solving
• Contribute to development and reuse of standardized AI “skills” or prompt patterns for engineering tasks
• Demonstrate familiarity with agentic AI concepts, including multi-step workflows driven by AI agents and tool integration
• Understand how external tools and services can be integrated into AI-driven workflows (e.g., MCP-style tool integration and context sharing)
• Evaluate opportunities to incorporate AI-driven automation into infrastructure and Dev Ops processes

• Lead or contribute to infrastructure design aligned with enterprise architecture, including high availability and disaster recovery
• Establish project milestones and drive execution to meet time, cost, and quality objectives
• Analyze, design, and implement solutions in complex and evolving environments
• Provide technical leadership and guidance across teams

• Investigate and resolve complex service issues across systems, platforms, and teams
• Perform root cause analysis and implement preventative solutions
• Participate in on-call rotation and provide production support
• Maintain operational documentation, standards, and runbooks

• Mentor team members and share knowledge across the organization
• Influence stakeholders, partners, and peers on infrastructure and platform best practices
• Stay current with industry trends, including Dev Ops, cloud-native platforms, and AI engineering practices
• Drive continuous improvement and innovation across systems and processes