Python Developer (AI)
Department: Engineering
Location: Belgrade, Lisbon, Remote
Employment Type: FullTime
We are looking for a solution-oriented AI Engineer who can design and build production-grade multi-agent LLM systems.
The key expectation is not just writing code, but understanding the desired user outcome and engineering systems that reliably deliver it.
This role is closer to systems engineering / architecture than classical ML.
You will work independently, explore our APIs and product flows, and build agent-based solutions that integrate deeply with the platform.
Context
Product platform with multiple internal APIs
Agents interact with platform APIs and product workflows
High variability of use cases and flows
Relatively small data volumes per agent, but high orchestration complexity
Agents must operate autonomously with tools, routing, memory and fallback strategies
Backend stack mainly Python, frontend TypeScript
Role Responsibilities
Design and implement multi-agent systems (orchestrator + worker agents)
Build agent infrastructure:
routing
memory
tool execution
fallback strategies
Develop multimodal pipelines (text β images)
Integrate and abstract different model providers
Build custom tools and MCP servers
Connect agents to internal platform APIs
Read and analyze platform API specs and source code (Python backend / TypeScript frontend)
Extend or build API layers / adapters when necessary
Implement automated testing and LLM evaluation pipelines
Debug agent behavior and improve reliability
Required Qualifications
Familiarity with the gamedev domain, including experience in analytics, payments, or similar areas of the industry
Strong Python backend development (async, APIs, services)
Commercial experience building multi-agent LLM systems
Experience with agent frameworks like:
LangChain
LangGraph
AutoGen
CrewAI
LlamaIndex
Pydantic AI
Experience designing:
agent orchestration
tool calling
routing
memory systems
Experience building multimodal pipelines (text + image generation)
Experience integrating multiple model providers:
OpenAI
Anthropic
local / self-hosted models
Vector databases: Qdrant / Pinecone / Weaviate
Redis or similar caching / state layers
Experience with production-grade development practices
testing
debugging
observability
Important: commercial experience is required (not pet projects).
Preferred Qualifications
ML background or practical ML knowledge
Experience designing AI product architectures
Experience building agent evaluation / benchmarking frameworks
Experience working with large API ecosystems
Experience with AI observability tools
Working Style
Strong ownership and autonomy
Ability to work from problem β architecture β implementation
Comfortable exploring unfamiliar codebases, APIs and product logic
Focus on engineering reliable AI systems, not just experimenting with models
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