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Cortea is building intelligent systems for a high-stakes domain where accuracy matters. You will join as a Staff Platform Engineer responsible for making the company's rapidly growing system explicit, reliable, and scalable.
Your core mission is to codify golden paths, build shared primitives and APIs for product engineers, own the system architecture, and establish reliability standards before incidents force them. Platform at Cortea sits between software engineering, DevOps, and SRE—your customers are other engineers. This is not a DevOps role: you will spend more time in application code than infrastructure configuration, and infrastructure experience is valued because you cannot design systems well without being able to run them.
You will work across four interconnected areas:
**Product Platform**: Design the easiest path for product engineers (the paved road) to also be the most secure, reliable, and scalable by default. Build shared primitives, libraries, and APIs that hide complexity while carrying quality and observability standards. Own the core stack architecture and long-term direction.
**Infrastructure, Observability & Reliability**: Implement infrastructure-as-code across cloud resources, dashboards, and alerts. Provision, run, and tune the Kubernetes cluster and cloud footprint. Define SLIs for critical workloads, build SLOs, and create high-signal alerting. Drive down AI and infrastructure spend.
**Security & Compliance**: Secure internal foundations by default (IAM, dependency management, secrets). Own the authentication and authorization stack, including ReBAC models for both humans and agents. Implement SOC 2 and ISO 27001 controls without creating friction for future changes.
**AI Dev Tooling**: Keep product engineers and their agents on the paved road through documentation, agent guidelines, and standardized automations. Shorten the path from design to implementation with development environments that mirror production.
Practical examples of work include: building a system of record for agent configurations with auditability and tenant isolation; creating harnesses that abstract over rapidly changing use cases; centralized progress tracking for parallel agent executions; usage-based tenant billing with quotas and rate limiting; SLIs for background job systems (execution latency, AI spend, memory, CPU); and a document pipeline handling dozens of file formats and hundreds of parallel uploads under strict reliability requirements.
The ideal candidate has designed, built, and operated distributed systems end-to-end; is strong in both backend software engineering and DevOps/SRE (not viewing them as separate); writes design docs, RFCs, ADRs, and postmortems; reaches for simple, boring solutions first; and can distinguish essential from accidental complexity. Concrete experience scaling Postgres or another relational database under load, running production Kubernetes, and building observability (SLIs, SLOs, distributed tracing) is expected.
You will work face-to-face with experienced founders, learn directly from customer insights, and have real influence on strategy within a small team of excellent engineers with high autonomy. Decisions happen in hours, not weeks. The team is fast, ambitious, and fun. You will receive meaningful equity and competitive salary as one of the first to build this platform.
**Requirements:**
- Designed, built, and operated distributed systems end-to-end
- Strong backend software engineering skills combined with DevOps/SRE expertise
- Experience scaling relational databases (e.g., Postgres) under production load
- Production Kubernetes experience
- Built observability from scratch (SLIs, SLOs, distributed tracing)
- Ability to write design docs, RFCs, ADRs, and postmortems
- Strong judgment about complexity, tradeoffs, and which corners are safe to cut
**Nice-to-haves:**
- Experience with durable workflow orchestrators (e.g., Temporal) and background job/queue processing
- Infrastructure for LLM-based products or agentic systems
- Background in audit, finance, compliance, or other high-accuracy domains
- Azure and/or GCP experience
