Agent Infrastructure Engineer — Core Harness (Superagent)

The role

You will own Superagent, the core agent harness that powers conversations, tool calls, and multi-step agentic workflows. You will work on the core orchestration loop, tool-calling infrastructure, context and memory management, streaming, retries, evaluation, observability, and performance.

What you will do

• Own the architecture, development, and evolution of Superagent.
• 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.
• Integrate and benchmark multiple LLM providers and models.
• Implement performance optimizations such as caching, batching, parallel tool execution, and prompt/context compression.
• Build deep observability and instrumentation across agent runs.
• Extend and customize underlying agent frameworks.
• Build reliable integrations with evolving AI and tool ecosystems.
• Work closely with product engineering teams to expose clean abstractions.

Required 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.

Nice to have

• 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.

Benefits

• Competitive salary and benefits package.

About the company

ImagineArt is a GenAI company that has built and shipped its own image generation model. The company has scaled with zero outside funding.