The DevOps 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.
Key Responsibilities
CI/CD & Automation Engineering
- 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
AI & LLM Deployment Operations
- 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
Infrastructure, Reliability & Cost Optimization
- Monitor infrastructure performance, system health, and AI workloads using observability and monitoring tools
- Optimize infrastructure for reliability, performance, and cloud cost efficiency
Security, Compliance & Governance
- 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
Cross-Functional Enablement
- 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
Documentation & Architecture Standards
- Maintain comprehensive documentation for DevOps workflows, system architecture, environments, and deployment standards
- Ensure operational readiness, auditability, and knowledge transfer across teams
Required Qualifications
- Minimum 5 years of hands-on DevOps 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
Preferred Qualifications
- Experience with MLOps platforms such as MLflow, SageMaker, 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
KPIs & Deliverables
- 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 DevOps documentation and workflows
