DevOps Engineer

Job Description

Insight Global is partnering with an AI Software Company whose product is an inspection and security platform that helps customers use drones, robots, and fixed cameras, and related systems to detect security and compliance in real world scenarios.

We are looking for an Infra, Dev Ops & QA Lead to own the operational backbone of our clients AI platform. This role is responsible for how the platform is packaged, deployed, tested, monitored, and validated across customer-hosted and customer-controlled environments. You will own the deployment and release systems that allow our team to ship safely, support customers confidently, and scale the platform without relying on manual heroics.

This is a hands‑on engineering role. You should be comfortable working across Docker, Docker Compose, Kubernetes readiness, CI/CD, Linux environments, GPU/runtime dependencies, release packaging, automated testing, observability, and deployment validation. You will also help us use modern AI coding and deployment tools safely by defining clear environments, guardrails, repo permissions, secrets boundaries, allowed commands, and automated validation steps before code or deployment changes are accepted.

This position requires to work onsite 5x/week in West Palm Beach and is targeting a salary of $100K-130K.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances.

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• Docker, Docker Compose, and K8s basics
• CI/CD, ideally Git Hub Actions
• Release packaging and rollback planning
• Customer-hosted deployment support
• Linux and environment troubleshooting
• GPU/runtime dependency management
• Model artifact packaging and deployment support
• Automated API/UI smoke testing
• Regression test automation
• Test data and deployment validation workflows
• Observability, logs, health checks, and diagnostics
• Secure environment configuration
• Secrets, permissions, and access boundary management
• AI-assisted development workflow experience
• Context engineering for engineering agents
• AI infra / deployment agent workflow design