AI Solutions Engineer

Aghanim is an integrated commerce, liveops automation, community engagement, and payments platform for video games.

Mobile games have traditionally depended on app stores for distribution, payments, and player relationships. We believe there is a better way. Aghanim helps game studios build direct relationships with players, sell directly, and build their future on their own terms. Today, more than 100 games worldwide are already building this future with Aghanim.

Our team brings together people across Los Angeles, New York, Seoul, Beijing, London, Lisbon, Belgrade and other locations around the globe, with deep expertise in gaming, fintech and technology. We move quickly, keep communication direct, and focus on getting things done. We believe the best people thrive when they have autonomy, ownership, and a stake in the company's success.

We are looking for a solution-oriented AI Solutions 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

1. Agent Architecture and System Design

• Design and implement multi-agent systems, including orchestrator and worker agents

• Define agent architecture, including routing, memory, tool usage, and fallback strategies

• Translate product requirements into scalable and reliable agent workflows

• Ensure systems are designed for robustness, maintainability, and extensibility

1. Agent Infrastructure and Development

• Build core infrastructure for agent execution, including tool calling and state management

• Develop and maintain multimodal pipelines (text and image generation)

• Integrate and abstract multiple model providers (e.g., OpenAI, Anthropic, self-hosted models)

• Implement custom tools and services to extend agent capabilities

1. Platform Integration

• Integrate agents with internal platform APIs and product workflows

• Analyze API specifications and existing codebases (Python backend and TypeScript frontend)

• Build adapters and integration layers where necessary

• Ensure reliable interaction between agents and platform systems

1. Reliability, Testing, and Evaluation

• Implement testing strategies for agent behavior and system reliability

• Build evaluation pipelines to measure LLM and agent performance

• Debug complex agent workflows and improve system stability

• Contribute to observability, logging, and monitoring of AI systems

1. Cross-functional Collaboration

• Work closely with Product and Engineering teams to define use cases and system behavior

• Provide input on AI architecture and system design decisions

• Contribute to best practices for building production-grade AI systems

Required Qualifications

• Strong Python backend development experience, including async programming and APIs

• Commercial experience building multi-agent LLM systems (required)

• Experience with agent frameworks such as LangChain, LangGraph, AutoGen, CrewAI, LlamaIndex, or similar

• Experience designing agent orchestration, tool usage, routing, and memory systems

• Experience building multimodal pipelines

• Experience integrating multiple model providers (e.g., OpenAI, Anthropic, self-hosted models)

• Experience working with vector databases (e.g., Qdrant, Pinecone, Weaviate)

• Experience with Redis or similar systems for caching and state management

• Strong understanding of production-grade development practices, including testing and debugging

• Ability to design systems that prioritize reliability over experimentation

Nice to have

• 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 → architecture → implementation

• Comfortable exploring unfamiliar codebases, APIs and product logic

• Focus on engineering reliable AI systems, not just experimenting with models