Job Description

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

.