DevOps Engineer (GCP)

Are you passionate about AI? 🤖 At Satori Analytics, we aim to change the world one algorithm at a time by bringing clarity to global brands through Data & AI. From cloud-based ecosystems for fintech to predictive models for airlines, our cutting-edge solutions cover the entire data lifecycle—from ingestion to AI applications. As a fast-growing scale-up, our team of 100+ tech specialists—including Data Engineers, Data Scientists, and more—delivers innovative analytics solutions across industries like FMCG, retail, manufacturing and FSI. Join us as we lead the data revolution in South-Eastern Europe and beyond! Together with a partnering company, we're looking for a a DevOps / Platform Engineer to own and evolve the infrastructure that keeps this platform reliable (AI agent evaluation platform), observable, secure, and fast to ship to. You'll work closely with backend, ML, and frontend engineers to make deploying and operating services boring, repeatable, and safe. What Your Day Might Look Like: Cloud infrastructure as code : Own and extend our Terraform estate across multiple GCP environments (base, core, obs, dev, test, prod), including GKE clusters, Cloud SQL (Postgres/MySQL), networking, buckets, and IAM. Drive the in-progress "Neo" platform rollout and the cutover/retirement of legacy infrastructure. Kubernetes & containers : Manage workloads on GKE, maintain Dockerfiles and Helm-style application configs for ~10 backend services, and tune autoscaling, resource limits, and pod disruption budgets. Maintain and improve our GitHub Actions pipelines : PR checks (Python/JS lint, type-check, tests), Terraform prechecks, image builds and pushes, auto-deploy, and DB-migration labelling/gating. Reduce build times and flakiness, and make deploys self-service for product teams. Data & messaging infrastructure : Operate Postgres, Redis, and Celery-based async workers; manage Alembic migrations, queue health, and backpressure for long-running simulation jobs. Observability : Own our monitoring stack — Grafana dashboards, ClickHouse, Langfuse (LLM tracing), and Celery queue metrics. Build alerting and SLOs so we catch issues before customers do. Security & secrets : Manage secret distribution, least-privilege IAM, and remediation tracking. Partner with engineering on findings in our security assessment process. Cost & reliability : Keep an eye on cloud and LLM-proxy (LiteLLM) spend, right-size resources, and improve resilience of the simulation and evaluation pipelines. You'll work with: Cloud: Google Cloud Platform (GKE, Cloud SQL, GCS, IAM); some AWS / IBM footprint IaC: Terraform (>= 1.14), multi-environment root modules Containers/orchestration: Docker, docker compose (local), Kubernetes / GKE CI/CD: GitHub Actions Backend: Python 3.13+ (managed with uv), Celery, FastAPI-style HTTP APIs; Node/Express services Data: PostgreSQL, MySQL, Redis, ClickHouse Observability: Grafana, Langfuse, custom Celery metrics LLM infra: LiteLLM proxy