AI Platform Engineer, Agent Systems IRC

Function:
Software Product Engineering

Experience:

5-10 years

Location:

Poland

Skills:

Agentic & Multi-Agent Systems, Claude code, Infrastructure, Langgraph, LLM, MCP, Orchestration, Python, Type Script

The company is building the agent platform for professional music production: the orchestration layer, tool interfaces, skills runtime, and context architecture that allow any AI agent to reason about and act on a music-production workflow.

You will lead the design of the orchestration loop, define how the engine’s capabilities are exposed to models, build the skills runtime that transforms a general‑purpose model into a domain specialist, and architect the context and memory systems that keep agents coherent across long creative sessions.

The object model is a song. The users are producers, musicians, and creatives. The domain has real‑time constraints, deep semantics, and no existing playbook.

#LI-OM1

• Five or more years shipping production platform or infrastructure software that other engineers have built on top of.
• Eighteen or more months of production experience building LLM agent systems, covering orchestration loops, tool use, and context management. We have no preference for a specific framework. We are equally interested in engineers who shipped on a provider‑agnostic framework such as Lang Graph and engineers who rejected frameworks entirely and built their own harness, provided you can articulate what you learned from the path you took.
• Demonstrated experience designing tool interfaces for LLM consumption. You can explain what makes a tool schema discoverable and usable by a model versus merely technically correct.
• Demonstrated experience building context, memory, or state‑management systems beyond framework defaults, including compaction, durable memory, or session persistence. You have diagnosed agent failures from raw execution traces and made targeted harness changes in response.
• Strong proficiency in Type Script and Python.
• Experience with the Model Context Protocol (MCP) or similar tool‑connectivity standards.

• Background in music production, audio engineering, or another creative‑tool domain, including as a serious hobbyist.
• Experience with real‑time audio systems, professional audio software, or other latency‑sensitive environments.
• Experience making a complex desktop or professional application agent‑accessible, in any domain with a rich object model (DAW, IDE, design tool, CAD).
• Experience building middleware or hook architectures that allow others to customize agent behavior without modifying core code.

• What You Will Own
• Tool interfaces. Define how the engine’s capabilities are exposed to LLMs as structured, discoverable tools. This includes schemas, semantic descriptions, scoped tool sets, input validation, and output parsing that a model can reliably produce and the harness can reliably consume. Designing a tool surface that models use well is a distinct discipline from designing an API for human developers, and you will own that discipline.
• Orchestration and control flow. Design and build the harness: the core loop and the machinery around it. This covers step sequencing, retries, timeouts, error recovery, fallback paths, and multi‑agent coordination where a workflow is split across sub‑agents with their own tools and context. You will evaluate whether to build this in‑house, adopt a framework, or extend an existing one. We have no commitment to any specific framework, and we will not build the platform on top of a single provider or model.
  
  A well‑reasoned argument for building our own harness is a welcome outcome of that evaluation.
• Skills runtime. Design the format, packaging, loading, and execution layer for the structured domain knowledge that turns a generic model into a music‑production specialist. This is our most distinctive platform primitive and it is largely greenfield.
• Context, memory, and state. Build the systems that keep agents performant and coherent across long, multi‑step creative workflows. This includes context compaction, short‑term working memory, durable cross‑session memory, session state persistence,…