Infrastructure Engineer

Epiq AI Labs is the innovation and engineering hub behind Epiq's next-generation AI platform for corporate legal departments and global law firms. You will build and operate the cloud infrastructure and automation behind Epiq AI Labs' AI platform, including infrastructure as code, Kubernetes, CI/CD, networking, secrets, and observability across Azure, AWS, and Epiq-managed on-prem GPU environments.

Responsibilities:

• Write, test, review, and maintain reusable Terraform modules and infrastructure automation for provisioning, environment configuration, and drift detection and remediation.
• Operate Kubernetes in production, including orchestration, autoscaling, resource governance, network policy, and cluster lifecycle management.
• Build and maintain CI/CD and GitOps release automation, improving build and test feedback and targeting zero-downtime rollouts through progressive delivery, safe rollback, and protection of in-flight requests and long-running jobs.
• Operate the Epiq-managed GPU infrastructure (cloud and on-prem) that serves Epiq AI's in-house models: cluster management and scheduling, utilization and cost, observability, and model-serving reliability.
• Define platform service-level objectives and build the metrics, tracing, alerting, and error-budget practices required to support them.
• Implement security and compliance infrastructure, including hardening, audit logging, data-residency controls, retention, legal hold, and audit evidence collection.
• Build and maintain network, secrets, key, credential, TLS, and certificate-lifecycle infrastructure.
• Use AI coding agents throughout development while owning the outcome , the design, specifications and test plans, code review, and verification of correctness, security, and maintainability , and ship major changes behind automated tests and controlled rollouts, with stakeholder validation where appropriate.
• Build and maintain AI-native developer tooling and technical design documentation, and contribute to architectural reviews.
• Participate in the on-call rotation and incident response; maintain runbooks, test backup and restore procedures, and turn incident findings into lasting fixes.

Requirements:

• 3+ years of professional experience in infrastructure engineering, platform engineering, or site reliability engineering.
• Experience building and operating production infrastructure, including on-call responsibility for systems you have built, improved, or maintained.
• Hands-on experience with at least one major cloud platform such as AWS, GCP, or Azure.
• Experience with infrastructure-as-code tools, particularly Terraform, including reusable module design.
• Production Kubernetes experience, including scaling, upgrades, resource limits, network policy, and troubleshooting.
• Ownership of CI/CD pipelines using GitHub Actions, Azure DevOps, or a comparable platform.
• Experience using observability tools such as Prometheus, Grafana, or OpenTelemetry to monitor service health, diagnose incidents, and track service-level objectives.
• Experience participating in production incident response and applying lessons from incident reviews.
• Experience managing secrets and certificates in production, including secure storage, rotation, and renewal.
• Proficiency in Python, Go, or a comparable language, with experience building, testing, and maintaining production tooling and automation.
• Demonstrated proficiency using AI coding agents in day-to-day development, with sound judgment about where they accelerate work and where they fail.
• Strong system-design and architecture experience, including production of technical design documents.
• Self-motivated, with high ownership: you solve problems end to end, actively keep up with new developments in AI, and quickly learn and apply new technologies and approaches to your work, including areas you haven't worked in before.