AI Pod - Cloud and Platform Engineer

Vacancy details Argentina|Costa Rica|MexicoArgentina|Costa Rica|Mexico RemoteRemote Recruiter Nataliia Olkhovska Nataliia Olkhovska n.olkhovska@svitla.com **Role overview** ----------------- Svitla Systems Inc. is looking for a **Cloud and Platform Engineer for the AI Pod** for a full-time position (40 hours per week) in LATAM. Svitla’s Forward Deployed AI Engineering Pod is a small, senior team that works inside our client’s environment to take the client’s AI workflows into production one Workflow at a time, measuring the business result of each. The client pays a fixed monthly fee for working capability, not hours or headcount, and directs which Workflow the Pod works on. Each Workflow runs through Discover, Foundation, Build, Prove (shadow mode against live outcomes), and Operate (handover with runbooks, evaluation harness, and training), adapted from CRISP-DM, with the client’s own engineers shadowing throughout. The Pod has up to five people in any month. Three Continuous Roles stay with the client for the whole engagement so that accountability never changes hands: the Outcome Lead, the Forward Deployed Technical Lead, and the Cloud and Platform Engineer. Specialist Bench roles (for example, agent and MCP engineering, data and semantic engineering, integration, document AI, evaluation, security architecture, data science, and adoption) rotate in as the active Workflow requires. The initial term is three months, then month to month for as long as the backlog holds Workflows the client wants in production. Continuous Role people are Svitla’s reusable core: they carry what they learn to the next Pod, reuse Svitla’s connectors, evaluation harnesses, governance patterns, and deployment templates, and improve them. As the Cloud and Platform Engineer, you will own identity, deployment, and operations for the Pod. You will stand up and run the environments, infrastructure as code, pipelines, secrets, least-privilege access, observability, audit logging, and cost visibility inside the client’s cloud, and you will secure the early security and DevOps sign-offs that let a Workflow move from shadow mode to production without surprises. **Requirements** ---------------- * Experience running ML or LLM workloads in production on at least one major cloud (AWS, Azure, or GCP), including deployment pipelines, monitoring, and incident handling. * Strong experience with infrastructure as code and CI/CD (Terraform or equivalent, GitHub Actions or equivalent, and containers and Kubernetes or a managed equivalent), with examples of environments built from scratch. * Hands-on experience with identity and access management: IAM policies, service accounts or workload identities, secrets management, and least-privilege design, with an audit or compliance review passed. * Working knowledge of a modern data platform (Databricks, Snowflake, BigQuery, or similar) and of how governed access (catalogs, row and column security) is enforced. * Experience with o