About the Role
We are looking for a Senior DevOps Engineer to help build, scale, and operate the infrastructure powering our AI platform. This is a hands-on, high-ownership role. We are looking for someone who can work independently, solve complex infrastructure problems, and thrive in a fast-paced startup environment. You should be comfortable designing systems, automating processes, troubleshooting production issues, and continuously improving reliability, scalability, and cost efficiency.
What You'll Own
- Design, build, and operate highly available production infrastructure on AWS, with strong expertise in EKS, EC2, VPC, S3, RDS/Aurora, IAM, ECR, ElastiCache, Load Balancers, and other core AWS services.
- Build and improve CI/CD and release automation using Jenkins, GitHub Actions, Helm, ArgoCD, and GitOps.
- Manage infrastructure using Terraform and Infrastructure as Code principles.
- Build and operate Kubernetes platforms at production scale, including cluster management, upgrades, autoscaling, networking, security, and troubleshooting.
- Run and operate AI/ML workloads in production, with a strong understanding of the infrastructure challenges associated with AI systems.
- Deploy, scale, monitor, and optimize AI inference and model-serving workloads across Kubernetes and cloud infrastructure.
- Work with GPU-based workloads, including GPU scheduling, utilization, autoscaling, capacity planning, and optimization.
- Drive infrastructure efficiency by balancing performance, reliability, scalability, and cost across AI workloads.
- Own monitoring, logging, and observability using tools such as Prometheus, Grafana, Thanos, and OpenTelemetry.
- Implement secure secrets management using technologies such as HashiCorp Vault and External Secrets Operator.
- Develop automation and internal tooling using Python and Bash.
- Drive improvements around reliability, security, scalability, performance, and infrastructure cost.
- Participate in production incidents, root-cause analysis, and drive long-term fixes rather than short-term workarounds.
- Work closely with Engineering, ML/AI, Security, and Product teams to solve infrastructure and platform challenges.
What We're Looking For
- 5+ years of strong hands-on experience in DevOps, SRE, Platform Engineering, or Cloud Infrastructure.
- Strong production experience with AWS and Kubernetes/EKS.
- Proven experience running AI/ML workloads or GPU-based workloads in production is highly valuable.
- Strong understanding of CI/CD, Infrastructure as Code, GitOps, observability, and cloud security.
- Excellent scripting and automation skills in Python and Bash.
- Experience operating production systems and troubleshooting complex infrastructure issues independently.
- Strong understanding of scaling, performance optimization, resource utilization, and cost management, particularly for compute-intensive workloads.
- Strong ownership mindset with the ability to take a problem from design to production.
- Experience working in a startup or fast-moving engineering environment is highly valued.
- Strong communication skills and the ability to work effectively across teams.
Nice to Have
- Experience with AI inference platforms, model serving, LLM infrastructure, or ML platforms.
- Experience with GPU infrastructure such as NVIDIA GPUs and Kubernetes GPU scheduling.
- Experience with multi-region or highly distributed systems.
- Experience with SOC 2, ISO 27001, or other security/compliance requirements.
- Experience with PostgreSQL, MongoDB, Redis, Kafka, or similar distributed systems.
The Kind of Engineer We Want
We're looking for someone who builds, automates, and takes ownership — not someone who simply operates existing infrastructure.
You should be comfortable with ambiguity, willing to dive deep into production problems, and constantly looking for ways to make our platform more reliable, secure, scalable, and cost-efficient.
Most importantly, you should understand that AI infrastructure has a different set of operational challenges. We want someone who can help us run AI systems efficiently at scale — making the right trade-offs between GPU utilization, performance, reliability, scalability, and cost.
This is a high-ownership role for someone who wants to make a meaningful impact on the infrastructure behind an AI platform as we scale.