platform engineer for AI platforms

Playamp is MTG’s Midcore District, bringing together six gaming studios that create games played by tens of millions of people across mobile and PC. It provides a shared ecosystem spanning marketing, data analytics, technology, player services, publishing, D2C distribution, and infrastructure, enabling each studio to focus on building games while benefiting from shared expertise and capabilities.

• Design, build, and operate Playamp’s internal AI platform, including the model gateway, agent orchestration, RAG pipelines, vector stores, and MCP servers connecting LLMs to internal systems
• Productionize AI infrastructure on GCP using Vertex AI, GKE, managed and self-hosted inference, Terraform, and GitOps
• Bring AI into DevOps and automation workflows
• Own the production agent lifecycle, including registry, versioning, observability, tracing, evaluations, cost tracking, and regression gates
• Share standard senior DevOps responsibilities, including production ownership, on-call, networking, security hardening, and incident response for the AI platform’s core infrastructure
• Develop guardrails that help security teams track and monitor AI usage across the company

• 5–7 Years of production infrastructure, DevOps, or platform engineering experience, including 2+ years dedicated to AI infrastructure involving real systems rather than POCs
• Practical experience with MCP and RAG, including building or integrating MCP servers and exposing internal systems to LLMs
• Experience designing and shipping production agentic systems with multi-step, tool-using agents, guardrails, retries, and evaluation
• Deep cloud experience, preferably GCP, and solid Kubernetes, networking, Infrastructure as Code, Terraform, CI/CD, and GitOps fundamentals
• Experience building or owning LLM/agent evaluation harnesses, including golden datasets, offline and online evaluations, CI regression gates, and production A/B testing of prompts and agents
• Strong Python skills and hands-on experience with modern LLM serving stacks and at least one industry-standard agent framework, such as Google ADK or LangChain
• Experience managing production AI spend, including prompt and semantic caching, model selection trade-offs, batch versus real-time routing, and per-team budgets and showback
• Будет плюсом: Cost engineering for AI

• Officially registered full-time employment
• Paid annual leave according to local regulations
• Medical support and paid leave
• Individual development plan and regular feedback
• Professional seminars, workshops, courses, and internal training programs
• Reimbursement of gym membership fees
• Benefits may vary by country