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

- 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.

- Market-competitive salary and benefits package.

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

📌 Agent Infrastructure Engineer — Core Harness (Superagent) (India)
🏢 ImagineArt
📍 India