AI Platform Engineer – Gameframe

Ironbelly Studios; Montreal; Hybrid; CAD 100,000 - 140,000; Full time

Job summary

A game development company in Montreal is seeking an AI Systems Architect to design and build the core AI orchestration layer for their innovative platform. The ideal candidate will have a solid background in AI systems, backend engineering, and real-time processing, helping to revolutionize game development through advanced multi-agent orchestration. Competitive salary and equity options offered.

Benefits

Health, dental, and vision insurance

Flexible PTO

Professional development budget

Hardware stipend

Qualifications

• 2+ years building production AI systems.
• Expert Python 3.11+ with FastAPI, async / await patterns.
• Deep neural network understanding including transformer architectures.

Responsibilities

• Collaborate with CTO to build AI orchestration layer.
• Design multi-agent systems for game development workflows.
• Implement real-time streaming AI responses.

Skills

AI / ML Systems Mastery

Backend Systems Engineering

Open-source contributions in AI / ML

Container orchestration

Tools

LangChain

FastAPI

PostgreSQL

Docker

Kubernetes

Job description

Ironbelly Studios is comprised of a talented, passionate and dedicated number of game developers from all corners of the globe. Our focus is on creating high quality entertainment experiences as well as providing outsourcing services at a fraction of the cost seen elsewhere.

From humble beginnings Ironbelly has grown to be one of the most trusted names in the indie development space, working on over 300 projects in our 15 years of service, ranging from AAA titles such as Gotham Knights down to small mobile titles. We bring passion and experience together and look for people who put games and their creation in the center of their world.

Transforming Game Development Through AI

GameFrame AI is revolutionizing how games are created. We're not just another AI wrapper; we're architecting the future of creative development

The Challenge : Design and build sophisticated real-time interfaces that seamlessly blend conversational AI with game development workflows, supporting multi-agent coordination, real-time streaming responses, and complex game engine integrations - all while scaling to 1000's of concurrent users

The Role : AI Systems Architect

As our AI Platform Engineer , you will collaborate with our CTO toarchitect and build the core AI orchestration layer that powers our revolutionary platform. You'll be designing novel multi-agent systems that understand game development at a semantic level and coordinate complex workflows across distributed VM infrastructure.

You'll work at the intersection of cutting-edge AI research and production-scale systems engineering , building technology that has never existed before : real-time, multi-user AI orchestration for game development.

What You'll Build

Advanced Multi-Agent Orchestration

• Design and implement LangGraph Swarm architectures with 7+ specialized agents working in concert
• Build custom ReAct agents with sophisticated tool-calling capabilities and error recovery
• Implement constitutional AI layers for safety, consistency, and domain-specific constraints
• Create agent communication protocols for coordinating complex, multi-step game development tasks
• Implement intelligent context management for game development workflows
• Create continuous learning systems that improve agent performance through user interactions

Production AI Infrastructure

Architect multi-provider LLM orchestration (OpenAI, Anthropic, Google) with intelligent fallback and cost optimization

Build hybrid memory systems combining conversation, entity, and semantic memory with Pinecone vector search

Implement real-time streaming AI responses integrated with WebSocket infrastructure

Design distributed agent systems that scale across multiple VM instances and user sessions

Production-Scale Architecture

Design session routing and VM orchestration for isolating user AI contexts

Build auto-scaling AI infrastructure that handles traffic spikes while optimizing costs

Implement comprehensive observability for AI systems with distributed tracing and performance monitoring

Create robust error handling and failover systems for production reliability

Technical Environment ️

AI / ML Stack

Core : LangChain, LangGraph, Langsmith, Langfuse

APIs : LiteLLM,OpenAI / Anthropic / Google

Memory : Neo4j, Graphiti,Pinecone, Weaviate, Redis, custom embedding systems.

Frameworks : Custom agent orchestration, constitutional AI layers

Evaluation : Custom testing frameworks, performance monitoring

Backend Infrastructure

API : FastAPI, Python 3.11+, async / await patterns

Database : PostgreSQL 15, TimescaleDB, Redis caching

Real-time : WebSocket, streaming protocols, pub / sub systems

Infrastructure : Docker, Kubernetes, AWS / GCP, auto-scaling, tailscale, Portainer,

Integration Layer

Game Engines : Unreal Engine, Unity

VM Orchestration : Custom session routing, multi-user isolation

Monitoring : Prometheus, Grafana, distributed tracing

Your Technical Expertise

Required : AI / ML Systems Mastery

2+ years building production AI systems

High-level LangChain / LangGraph experience including custom agents, state machines, memory systems, and chain optimization

Deep neural network understanding including transformer architectures, attention mechanisms, embedding spaces, and model limitations

Production RAG systems with advanced chunking strategies, reranking, hybrid search, and context optimization

Multi-provider LLM integration with experience across OpenAI, Anthropic, Google, and local model orchestration

Required : Backend Systems Engineering

Expert Python 3.11+ with FastAPI, async / await patterns, and microservices architecture

Production database systems including PostgreSQL optimization, TimescaleDB, and Redis caching strategies

Real-time systems with WebSocket management, streaming protocols, and low-latency processing

Container orchestration with Docker, Kubernetes, and cloud infrastructure (AWS / GCP)

Distributed systems design patterns, fault tolerance, and observability

Preferred : AI Research & Innovation

Open-source contributions in AI / ML

Advanced prompt engineering including few-shot, chain-of-thought, and tree-of-thought techniques

Evaluation frameworks for automated testing of AI agent performance and prompt regression testing

Mixture of Experts patterns and agent specialization architectures

Your Impact & Growth

Immediate Impact (First 90 Days)

Architect core agent systems for Movement and Combat specialists

Implement multi-provider LLM orchestration with cost optimization

Build production memory systems with Pinecone integration

Design evaluation frameworks for agent performance monitoring

Platform Impact (6-12 Months)

Scale to 500+ concurrent users with auto-scaling infrastructure

Launch advanced agent capabilities including code generation and semantic understanding

Implement continuous learning systems that improve through user interactions

Build enterprise-grade observability and performance optimization

Industry Impact (12+ Months)

Establish new patterns for AI-driven creative software architecture

Contribute to open source and research in multi-agent systems

Scale platform to support thousands of developers creating games

Lead technical strategy as the platform evolves toward enterprise studios

Benefits

Base Salary commensurate with experience

Equity : Significant equity package with high growth potential

Benefits Package :

Health, dental, vision with company contribution

Flexible PTO and remote / hybrid work arrangements

Professional development budget for AI / ML conferences and research

Hardware stipend for development equipment and cloud resources

Research time allocated for staying current with AI developments

Unique Perks :

Conference speaking opportunities as we establish thought leadership

Open source contribution time for projects aligned with platform goals

Direct access to cutting-edge AI models and research partnerships

Creative project support for personal game development experiments