Senior platform engineer

Description Fetcherr experts in deep learning, e-commerce, and digitization, Fetcherr disrupts traditional systems with its cutting-edge AI technology. At its core is the Large Market Model (LMM), an adaptable AI engine that forecasts demand and market trends with precision, empowering real-time decision-making. Specializing initially in the airline industry, Fetcherr aims to revolutionize industries with dynamic AI-driven solutions. Fetcherr is scaling rapidly, and we are transforming our internal infrastructure into a high-velocity, self-service product. This is a senior opportunity to architect the future of our platform, setting the technical standard for how we deliver services while ensuring reliability and developer independence across diverse global markets. What you'll do Self-service enablement: Build secure, automated pathways that accelerate development while keeping organizational standards intact. Architectural modularization: Design Kubernetes-native abstractions — controllers and operators — and decouple infrastructure into independent service lifecycles that support rapid iteration. Everything as code: Treat infrastructure, policy, and configuration with the same SDLC as application software — versioned, reviewed, tested, and CI-gated. Proactive reliability: Own production through robust observability, clear SLOs, and high operational hygiene; define alerting that fires correctly and lead blameless postmortems that fix the system. Requirements Strong GCP experience at production scale (GKE, IAM, networking, Cloud SQL/BigQuery). No compromise here. Kubernetes-native mindset: declarative-first, comfortable with controllers and operators, and a platform-engineering instinct for self-service and separation of concerns. Everything-as-code discipline: any code ships through a defined SDLC (versioned, reviewed, tested, CI-gated). Terraform (KCC a plus), Helm, GitOps (ArgoCD or similar) in your toolkit. Software engineering background, or proven strong development ability in Go and/or Python — you write production code, not just glue scripts. Security awareness baked into how you build: least-privilege IAM, secret hygiene, policy and supply-chain gates, not bolted on after. Production-first mindset: you design for reliability, observability, and recoverability, and you own what you ship. Nice to have Policy-as-code (OPA/Conftest, Kyverno) and supply-chain/CI security tooling. SLO/error-budget practice and DORA-based delivery measurement. Multicloud experience (AWS / Azure) — GCP is home, but breadth helps. Big Data or MLOps exposure (Airflow, Dagster, Ray, Kubeflow). A point of view on AI-assisted and spec-driven development.