DevOps Engineer

Clinically AI•San Diego, CA

2d•$125,000 - $160,000

About The Position

Clinically AI is a rapidly scaling healthcare AI company transforming how behavioral health and healthcare organizations manage clinical documentation, compliance workflows, chart auditing, and operational efficiency through artificial intelligence. Our platform helps clinicians, compliance teams, administrators, and healthcare organizations reduce administrative burden, improve documentation quality, strengthen audit readiness, and operate more efficiently. We operate at the intersection of AI, healthcare operations, workflow design, and real-world clinical execution, where security, reliability, scalability, and performance matter. We are seeking a DevOps Engineer to own and evolve the infrastructure, deployment systems, and cloud environments that power Clinically AI's product platform. This is a hands-on engineering role for someone who thrives in modern cloud-native environments and enjoys solving complex infrastructure, reliability, security, and deployment challenges. You will work closely with Backend Engineering, AI, Product, and Engineering leadership to build and maintain the infrastructure that supports browser extensions, mobile applications, backend services, real-time AI processing, data pipelines, and customer-facing applications. You should be comfortable operating with significant ownership and autonomy across GCP, Terraform, Helm, Kubernetes, CI/CD, observability, and containerized architectures. As we scale, you will have the opportunity to influence infrastructure strategy, deployment practices, security standards, and engineering culture.

Requirements

3+ years of experience in DevOps, SRE, Infrastructure Engineering, or a related role in cloud-native environments.
Strong experience with Terraform for provisioning, versioning, and managing cloud infrastructure.
Hands-on experience working with GKE or another production-grade Kubernetes environment.
Experience writing, maintaining, and optimizing Helm charts for application deployments.
Strong understanding of Docker, containerization, microservices architecture, and the Kubernetes ecosystem.
Experience building and maintaining CI/CD pipelines, ideally using GitHub Actions.
Familiarity with GCP services including Cloud Run, Cloud Functions, GCS, Pub/Sub, IAM, Cloud SQL, and Secrets Manager.
Strong understanding of monitoring and observability tools such as Grafana, Prometheus, OpenTelemetry, or Google Cloud Monitoring.
Solid understanding of networking fundamentals including DNS, load balancing, VPCs, ingress/egress policies, and service meshes.
Strong communication skills, ownership mindset, and comfort working in a fast-paced startup environment.

Nice To Haves

Experience with security and compliance frameworks such as HIPAA, SOC 2, NIST, or CIS Benchmarks.
Experience supporting AI/ML infrastructure, GPU workloads, or high-volume data pipelines.
Experience optimizing cost, performance, and reliability in multi-region or high-availability cloud architectures.
Familiarity with distributed tracing, log aggregation, and advanced observability patterns.
Experience with feature flag systems, canary deployments, blue-green deployments, or progressive delivery.
Experience working in a fast-scaling startup or high-growth engineering organization.

Responsibilities

Design, build, and maintain cloud infrastructure on GCP, with a focus on GKE, container orchestration, and secure multi-environment deployments.
Build and maintain Infrastructure-as-Code using Terraform to ensure infrastructure is reproducible, versioned, and automated.
Develop and maintain Helm charts for consistent service deployments, upgrades, rollbacks, and environment management.
Improve and optimize CI/CD pipelines using GitHub Actions to support reliable deployments across staging, beta, and production.
Partner with engineering leadership to design scalable infrastructure for new services, AI pipelines, and high-throughput workloads.
Implement robust observability across logging, metrics, tracing, alerting, and system health.
Drive infrastructure security best practices, including secrets management, least-privilege IAM, zero-trust patterns, and compliance alignment.
Optimize infrastructure performance, reliability, and cost while maintaining strong availability and security requirements.
Participate in on-call rotations, incident response, and post-incident reviews to continuously improve platform reliability.
Partner closely with Backend, AI, and Product teams to support new features, data pipelines, and infrastructure requirements as the platform grows.