Agent Infrastructure Engineer — Core Harness (Superagent)

About this role

ABOUT IMAGINEART We're redefining how the world creates and designs. ImagineArt is one of the fastest-growing GenAI companies in the world. We've scaled faster than most funded startups — with zero outside funding. - $35M+ ARR crossed this year - 100M+ social impressions - Built and shipped our own image generation model, now ranked #3 globally for photo realism No funding. No shortcuts. Just a sharp, driven team building one of the strongest GenAI products in the world — and we're just getting started. We're looking for an Agent Infrastructure Engineer to own Superagent, our core agent harness that powers conversations, tool calls, and multi-step agentic workflows across our AI products. This is a deep systems and infrastructure role — not prompt engineering and not simply wrapping model APIs. You'll work on the core orchestration loop, tool-calling infrastructure, context and memory management, streaming, retries, evaluation, observability, and performance. KEY RESPONSIBILITIES - Own the architecture, development, and evolution of Superagent, our core agent harness. - Design and optimize the agent execution loop for latency, reliability, token efficiency, cost, and task completion. - Build and improve core harness systems including context management, memory/state handling, tool routing, function schemas, structured outputs, retries, and error recovery. - Build and maintain agent evaluation infrastructure to measure quality and guide engineering decisions with data. - Integrate and benchmark multiple LLM providers and models, evaluating performance, cost, reliability, and capabilities. - Implement performance optimizations such as caching, batching, parallel tool execution, and prompt/context compression. - Build deep observability and instrumentation across agent runs, including tracing, logging, metrics, and regression detection. - Extend and customize underlying agent frameworks when existing abstractions are insufficient. - Build reliable integrations with evolving AI and tool ecosystems. - Work closely with product engineering teams to expose clean abstractions while keeping harness complexity behind the platform. - Debug and resolve complex issues across non-deterministic, distributed, and model-driven systems. REQUIRED SKILLS & QUALIFICATIONS - 4+ years of experience in software engineering, backend engineering, or systems infrastructure. - Strong proficiency in Python and/or TypeScript. - Hands-on experience building or operating LLM-based agents in production. - Strong understanding of tool calling, function schemas, context limits, structured outputs, model failures, and unreliable LLM behavior. - Experience with at least one agent framework such as LangGraph, OpenAI Agents SDK, CrewAI, AutoGen, or a custom/homegrown agent harness. - Strong understanding of agent orchestration and multi-step workflows. - Experience building or working with evaluation suites, benchmarks, A/B testing, or other measurement systems for AI products. - Strong understanding of concurrency, caching, profiling, performance optimization, and latency/cost tradeoffs. - Experience working with LLM APIs and production AI infrastructure. - Excellent debugging and problem-solving skills, especially for complex and non-deterministic systems. - Passionate about technology, self-driven, and proactive with a strong builder mindset. OPTIONAL / NICE-TO-HAVE SKILLS - Contributions to open-source agent frameworks, LLM tooling, or AI infrastructure. - Experience with RAG pipelines, vector databases, or long-term memory systems for AI agents. - Familiarity with MCP (Model Context Protocol) or similar tool-integration standards. - Experience with LLM inference infrastructure, model routing, rate limits, fallbacks, or high-volume model APIs. - Experience with LangChain, LlamaIndex, LangGraph, DSPy, or similar AI infrastructure frameworks. - Experience with Kubernetes, Docker, cloud infrastructure, or distributed systems. - Experience building internal developer platforms or infrastructure used by multiple engineering/product teams. - Strong background in observability, distributed tracing, and production reliability. - Contributions to open-source projects or personal AI infrastructure projects. WHY JOIN US? - Own the core agent infrastructure behind our AI products — every improvement you make can multiply across the entire platform. - Work on real production-scale AI systems, not demo agents or simple API wrappers. - Solve challenging problems across LLMs, distributed systems, orchestration, performance, and infrastructure. - Have direct influence over the architecture and technical roadmap of our entire agent stack. - Collaborate with a passionate and talented team building some of the most ambitious GenAI products in the market. - Competitive salary and benefits package. - A culture that encourages ownership, experimentation, learning, and data-driven engineering.