Senior AI Platform Engineer

• 5-7 years of Backend Engineering\ infrastructure\ DevOps \ 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).

• 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.

• 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.

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.

• 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 automationworkflows.

• 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.