Asks for

The ad says“Experience building or working with evaluation suites, benchmarks, A/B testing, or other measurement systems for AI products.”
The ad says“Experience with at least one agent framework such as LangGraph, OpenAI Agents SDK, CrewAI, AutoGen, or a custom/homegrown agent harness.”
The ad says“Experience with at least one agent framework such as LangGraph, OpenAI Agents SDK, CrewAI, AutoGen, or a custom/homegrown agent harness.”
The ad says“Strong understanding of concurrency, caching, profiling, performance optimization, and latency/cost tradeoffs.”
The ad says“Strong understanding of concurrency, caching, profiling, performance optimization, and latency/cost tradeoffs.”
The ad says“Strong understanding of tool calling, function schemas, context limits, structured outputs, model failures, and unreliable LLM behavior.”
The ad says“Experience with at least one agent framework such as LangGraph, OpenAI Agents SDK, CrewAI, AutoGen, or a custom/homegrown agent harness.”
The ad says“Experience building or working with evaluation suites, benchmarks, A/B testing, or other measurement systems for AI products.”
The ad says“Strong understanding of tool calling, function schemas, context limits, structured outputs, model failures, and unreliable LLM behavior.”
The ad says“Experience with at least one agent framework such as LangGraph, OpenAI Agents SDK, CrewAI, AutoGen, or a custom/homegrown agent harness.”
The ad says“Experience working with LLM APIs and production AI infrastructure.”
The ad says“Hands-on experience building or operating LLM-based agents in production.”
The ad says“Experience with at least one agent framework such as LangGraph, OpenAI Agents SDK, CrewAI, AutoGen, or a custom/homegrown agent harness.”
The ad says“Strong understanding of concurrency, caching, profiling, performance optimization, and latency/cost tradeoffs.”
The ad says“Strong understanding of concurrency, caching, profiling, performance optimization, and latency/cost tradeoffs.”
The ad says“Strong proficiency in Python and/or TypeScript.”
The ad says“Strong understanding of tool calling, function schemas, context limits, structured outputs, model failures, and unreliable LLM behavior.”
The ad says“Strong understanding of tool calling, function schemas, context limits, structured outputs, model failures, and unreliable LLM behavior.”
The ad says“Strong proficiency in Python and/or TypeScript.”

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.

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.