Infrastructure / DevOps Engineer
About Discernis
Discernis builds AI driven document intelligence for high stakes legal work. Because our customers handle privileged and regulated matters that most cloud AI cannot touch, we run on premises and in customer controlled environments as well as in the cloud. That makes security, reliability, and reproducibility core product features, not afterthoughts. We work with AmLaw firms and enterprise legal teams where accuracy, explainability, and data control are non negotiable.
The Role
You will own the infrastructure that runs our platform in three very different places: our cloud, customer managed cloud accounts, and customer controlled hardware, including air gapped and on premises environments. The hard and interesting part of this job is making the same product deploy reliably and reproducibly on machines we do not operate. If you like turning bespoke deployments into a repeatable system, this is that job.
What You Will Do
Package and ship our platform as a reproducible Kubernetes based bundle that installs cleanly on customer controlled and on premises hardware
Operate and improve Kubernetes clusters running both application services and GPU accelerated AI workloads
Own our infrastructure as code and release process so environments are reproducible across cloud and on prem
Build production grade monitoring, logging, and alerting that works even in restricted customer environments
Support large scale search and data infrastructure running on Kubernetes
Drive best practices in networking, secrets management, certificate handling, and security hardening
What You Bring
Solid experience running production Kubernetes
Experience with infrastructure as code tools such as Helm, Terraform, or Pulumi
Hands on experience designing CI and CD workflows
Comfort with observability stacks and production monitoring
Strong networking and Linux fundamentals
Bonus: GPU and AI workloads in Kubernetes, NVIDIA drivers and CUDA on bare metal, or shipping software into air gapped or customer controlled environments
Bonus: Elasticsearch or similar distributed data systems
Tech Environment
Kubernetes and K3s, Docker, NVIDIA GPUs, Elasticsearch, Terraform and Helm, CI/CD tooling, cloud and on premises environments
Skills Required
- Solid experience running production Kubernetes
- Experience with infrastructure as code tools such as Helm, Terraform, or Pulumi
- Hands-on experience designing CI/CD workflows
- Comfort with observability stacks and production monitoring
- Strong networking and Linux fundamentals
- GPU and AI workloads in Kubernetes, NVIDIA drivers and CUDA on bare metal, or deploying software into air-gapped or customer-controlled environments
- Experience with Elasticsearch or similar distributed data systems
What We Do
Discernis builds AI-native document-intelligence software for legal professionals handling litigation, investigations, and other high-stakes work. Its platform analyzes entire document collections—often millions of files—using cross-document analysis to surface key evidence and inform case strategy. Designed for security-sensitive customers, it can run on premises, in a customer-controlled cloud, or in hybrid environments, keeping inference within the organization rather than using third-party AI APIs.
