AI Platform Engineer

About the Company

This role is based in the Midcore District, a business unit within MTG that Plarium is part of. The Midcore District is home to six gaming studios: Plarium, InnoGames, Snowprint, Hutch, Ninja Kiwi, and Futureplay. Together, these studios make games played by tens of millions of people on mobile and PC. The District isn’t just a holding structure. It offers studios a shared ecosystem covering marketing, data analytics, technology, player services, publishing, D2C distribution, and more so that they can focus on making great games.

About the Role

As a Senior AI Platform Engineer in the AI Lab team, you will take ownership of Playamp's AI platform infrastructure and play a key role in enabling teams across the company to build and scale AI-powered solutions. In this role, you will work closely with DevOps, Security, Engineering, and Product teams to support both production infrastructure and the development of Playamp's internal AI platforms.

Responsibilities

• Design, build, and operate Playamp's internal AI platform - model gateway, agent orchestration, RAG pipelines, vector stores, and the MCP servers that connect LLMs to our internal systems.
• Productionize AI infrastructure on GCP (Vertex AI, GKE, managed and self-hosted inference) using Terraform and GitOps.
• Bring AI to our DevOps and automation workflows.
• Own the agent lifecycle in production: registry, versioning, observability (tracing, evals, cost tracking), and regression gates.
• Carry standard senior DevOps responsibilities alongside the team: production ownership, on-call, networking, security hardening, and incident response on AI platform's core infrastructure.
• Develop guardrails that help the security teams track and monitor AI usage across the company.

Qualifications

• 5-7 years of infrastructure, DevOps, or platform engineering in production, including 2+ years dedicated to AI infrastructure (real systems, not POCs).
• Practical experience with the Model Context Protocol (MCP) and RAG - building or integrating MCP servers and exposing internal systems to LLMs.
• Experience designing and shipping agentic systems in production: multi-step, tool-using agents with guardrails, retries, and evaluation.
• Deep cloud experience, preferably GCP, with solid Kubernetes, networking, Infrastructure as Code (Terraform), CI/CD, and GitOps fundamentals.
• AI evaluation infrastructure: Built or owned eval harnesses for LLMs/agents - golden datasets, offline + online evals, regression gates in CI, A/B testing of prompts and agents in production.
• Strong Python. Hands-on with the modern LLM serving stack and at least one industry-standard agent framework (e.g., Google ADK, LangChain, or similar).

Required Skills

• Cost engineering for AI.
• Practical experience managing AI spend in production - prompt and semantic caching, model selection trade-offs, batch vs real-time routing, per-team budgets and showback.

What We Offer

• 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.
• *Note: Benefits may vary by country.

Equal Opportunity Statement

We are committed to diversity and inclusivity.