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
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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
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Own the architecture, development, and evolution of Superagent , our core agent harness.
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Design and optimize the agent execution loop for latency, reliability, token efficiency, cost, and task completion.
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Build and improve core harness systems including context management, memory/state handling, tool routing, function schemas, structured outputs, retries, and error recovery .
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Build and maintain agent evaluation infrastructure to measure quality and guide engineering decisions with data.
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Integrate and benchmark multiple LLM providers and models , evaluating performance, cost, reliability, and capabilities.
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Implement performance optimizations such as caching, batching, parallel tool execution, and prompt/context compression .
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Build deep observability and instrumentation across agent runs, including tracing, logging, metrics, and regression detection.
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Extend and customize underlying agent frameworks when existing abstractions are insufficient.
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Build reliable integrations with evolving AI and tool ecosystems.
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Work closely with product engineering teams to expose clean abstractions while keeping harness complexity behind the platform.
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Debug and resolve complex issues across non-deterministic, distributed, and model-driven systems.
Required Skills & Qualifications
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4+ years of experience in software engineering, backend engineering, or systems infrastructure.
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Strong proficiency in Python and/or TypeScript .
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Hands-on experience building or operating LLM-based agents in production .
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Strong understanding of tool calling, function schemas, context limits, structured outputs, model failures, and unreliable LLM behavior .
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Experience with at least one agent framework such as LangGraph, OpenAI Agents SDK, CrewAI, AutoGen , or a custom/homegrown agent harness.
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Strong understanding of agent orchestration and multi-step workflows .
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Experience building or working with evaluation suites, benchmarks, A/B testing, or other measurement systems for AI products.
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Strong understanding of concurrency, caching, profiling, performance optimization, and latency/cost tradeoffs .
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Experience working with LLM APIs and production AI infrastructure .
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Excellent debugging and problem-solving skills, especially for complex and non-deterministic systems.
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Passionate about technology, self-driven, and proactive with a strong builder mindset .
Optional / Nice-to-Have Skills
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Contributions to open-source agent frameworks, LLM tooling, or AI infrastructure .
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Experience with RAG pipelines, vector databases, or long-term memory systems for AI agents.
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Familiarity with MCP (Model Context Protocol) or similar tool-integration standards.
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Experience with LLM inference infrastructure , model routing, rate limits, fallbacks, or high-volume model APIs.
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Experience with LangChain, LlamaIndex, LangGraph, DSPy , or similar AI infrastructure frameworks.
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Experience with Kubernetes,