DevOps Engineer

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The Dev Ops Engineer will play a mission-critical role owning the deployment, scalability, security, and reliability of AI systems and digital platforms. This role has a strong focus on LLM deployments, AI workloads, and cloud-native infrastructure
, ensuring that all AI and software systems operate with enterprise-grade availability, performance, and compliance.

• Design, build, and maintain CI/CD pipelines for AI models, LLM services, and software applications
• Automate build, test, deployment, and environment configuration workflows to enable rapid and reliable releases

• Deploy, operate, and scale AI systems, LLM APIs, inference workloads, and cloud-based AI services
• Ensure high availability, horizontal scalability, and low-latency inference across all production environments

• Monitor infrastructure performance, system health, and AI workloads using observability and monitoring tools
• Optimize infrastructure for reliability, performance, and cloud cost efficiency

• Implement and enforce security best practices, access controls, secrets management, and environment isolation
• Ensure infrastructure and deployment processes align with national data governance, compliance, and cybersecurity standards

• Collaborate closely with AI Engineers, Full-Stack Engineers, and Product teams to enable seamless, scalable deployments
• Act as the primary technical owner for production reliability during mission-critical deployments

• Maintain comprehensive documentation for Dev Ops workflows, system architecture, environments, and deployment standards
• Ensure operational readiness, auditability, and knowledge transfer across teams

• Minimum 5 years of hands-on Dev Ops engineering experience in production environments
• Mandatory:
  Proven experience deploying and operating AI systems and LLM-based workloads in production
• Strong hands-on expertise with Docker, Kubernetes, CI/CD platforms, and cloud services
• Experience with monitoring, observability, logging, and infrastructure-as-code (e.g., Terraform, similar tools)
• Strong understanding of networking, security, and cloud-native architecture principles
• Excellent troubleshooting and incident response capabilities in high-availability systems

• Experience with MLOps platforms such as MLflow, Sage Maker, Vertex AI, or similar
• Proven experience scaling AI and LLM applications in high-traffic production environments
• Exposure to AI model lifecycle management, retraining pipelines, and operational governance
• Experience in government, regulated, or national-scale enterprise environments

• Uptime, reliability, and stability of AI platforms and production systems
• Deployment speed, automation maturity, and release reliability
• Infrastructure performance, scalability, and cost optimization efficiency
• Security posture and compliance readiness across all environments
• Quality, completeness, and audit readiness of Dev Ops documentation and workflows

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