Site Reliability Engineer

About the role:

As a Site Reliability Engineer at DeepJudge, you own the reliability of the systems that leading law firms depend on every day. We run our AI search platform in dedicated production environments, each a large-scale search and retrieval system operating at very high data volumes, alongside our ingestion pipelines and data stores, on container-orchestrated infrastructure across multiple public and private clouds. Keeping those environments fast, available, and trustworthy is a genuine engineering challenge, and it's your mission.

This is a technical, infrastructure-focused role: you'll spend your time in systems, code, and telemetry. You'll harden the platform so that classes of failure can't recur, build observability and SLOs to let us see problems before they surface, plan capacity ahead of relentless data growth, and automate away the toil of safely operating many production environments. You'll work across our deployment and infrastructure-as-code and partner closely with product engineering to feed reliability learnings back into the platform.

If you like operating serious distributed systems at scale and turning firefighting into engineered reliability, this role is for you.

Your responsibilities will include:

• Owning the availability, latency and performance of our production environments: large-scale search and retrieval services, ingestion and data stores on container-orchestrated infrastructure across multiple public and private clouds
• Owning the cloud infrastructure across multiple public and private clouds, networking, identity and access, compute and storage, and managing cloud capacity, quotas and cost as the fleet grows
• Improving deployment and infrastructure through code: GitOps-based configuration and deployment and infrastructure-as-code across our public and private clouds, with safe, previewed, recoverable production changes
• Defining and instrumenting SLOs and error budgets and building the dashboards, metrics and alerting that make production observable and actionable
• Reducing operational toil through automation and internal tooling written in Python and / or Go
• Provisioning and standardizing new production environments to a defined reliability bar

You're a great fit if you:

• Have 3+ years in SRE, production / platform engineering, or infrastructure-heavy software engineering, operating real production systems under reliability expectations
• Are strong at container-orchestration operations in production and comfortable on at least one major public cloud
• Have solid cloud infrastructure experience on a major public cloud and a working grasp of cloud capacity, quotas, and cost
• Practice observability hands-on with metrics, logs and traces, and use it to root-cause live incidents
• Work fluently with infrastructure-as-code and GitOps
• Can code in Python and / or Go for automation, tooling, and reading and patching service code.
• Bring sound production judgment and document your work clearly for other engineers
• Bonus: experience operating a large-scale search or distributed data system, database operations, dedicated / enterprise-deployed software, or AI / enterprise-search infrastructure in regulated domains

What you can look forward to:

• Owning the reliability of systems that the world's leading law firms rely on: real scale, real impact
• Deep, hands-on work with modern infrastructure: distributed search at very large scale, container orchestration across multiple clouds, and a mature infrastructure-as-code and GitOps stack
• A rigorous, high-urgency engineering culture founded by ex-Google search engineers and ETH Zurich AI PhDs
• The chance to build the reliability discipline, SLOs, incident practice, automation, from a strong foundation, not inherit a rigid one
• A collaborative team in Zurich shaping how AI transforms professional knowledge work