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, Docker, cloud infrastructure , or distributed systems.

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Experience building internal developer platforms or infrastructure used by multiple engineering/product teams.

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Strong background in observability, distributed tracing, and production reliability .

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Contributions to open-source projects or personal AI infrastructure projects.

Why Join Us?
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Own the core agent infrastructure behind our AI products — every improvement you make can multiply across the entire platform.

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Work on real production-scale AI systems , not demo agents or simple API wrappers.

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Solve challenging problems across LLMs, distributed systems, orchestration, performance, and infrastructure .

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Have direct influence over the architecture and technical roadmap of our entire agent stack.

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Collaborate with a passionate and talented team building some of the most ambitious GenAI products in the market.

- Competitive salary and benefits package.

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A culture that encourages ownership, experimentation, learning, and data-driven engineering .