Agent Infrastructure Engineer — Core Harness (Superagent)

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. Originally posted on ...