Agent Infrastructure Engineer – Core Harness, Superagent

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ImagineArt

WebsiteLinkedInAll Job Openings

51 - 200 employees

🤖 Artificial Intelligence

☁️ SaaS

📱 Media

Artificial Intelligence • SaaS • Media

ImagineArt is an AI-powered creative platform that lets users and teams generate, edit, and manage images, videos, and other multimedia assets using generative models, workflows, and customizable apps. The product includes features such as inpainting, upscaling, motion control, lip-sync, background replacement, and mobile apps, and supports collaborative workflows for individuals and teams in production settings.

📋 Description

• Own the architecture, development, and evolution of Superagent, the core agent harness powering conversations, tool calls, and multi-step agentic workflows • Design and optimize the agent execution loop for latency, reliability, token efficiency, cost, and task completion • Build and improve 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 • Implement caching, batching, parallel tool execution, and prompt/context compression • Build 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 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

🎯 Requirements

• 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 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 for complex and non-deterministic systems • Passion for technology, self-driven, proactive, and possessing a strong builder mindset • Optional: contributions to open-source agent frameworks, LLM tooling, or AI infrastructure • Optional: experience with RAG pipelines, vector databases, or long-term memory systems • Optional: familiarity with MCP or similar tool-integration standards • Optional: experience with LLM inference infrastructure, model routing, rate limits, fallbacks, or high-volume model APIs • Optional: experience with LangChain, LlamaIndex, LangGraph, DSPy, or similar AI infrastructure frameworks • Optional: experience with Kubernetes, Docker, cloud infrastructure, or distributed systems • Optional: experience building internal developer platforms or infrastructure used by multiple engineering/product teams • Optional: strong background in observability, distributed tracing, and production reliability • Optional: contributions to open-source projects or personal AI infrastructure projects

🏖️ Benefits

• Competitive salary and benefits package • Culture that encourages ownership, experimentation, learning, and data-driven engineering • Direct influence over the architecture and technical roadmap • Collaboration with a passionate and talented team • Work on real production-scale AI systems