DevOps Engineer

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
  .