Agent Infrastructure Engineer — Core Harness (Superagent)
ImagineArt
IndiaremotePosted 3 days ago
Skill Required
Agent-Harness-EngineeringAI-Agent-Platform-EngineerAgent-EngineerAgent-Orchestration-EngineerAI-Agent-Systems-EngineerInfrastructure-EngineerInfrastructure Engineersoftware engineeringSystem DesignGenerative AIObservabilityStatisticsEngineeringTypeScriptKubernetesLangChaindebuggingbuildingTestNGPythonDockerdesignOpenAPICloudAPIsRAGandAIFulltime
Key highlights
- 4+ years of experience required
- Competitive salary and benefits package
- Required: Hands-on experience building LLM-based agents in production
- Focus: Deep systems and infrastructure (not prompt engineering)
Role overview
ImagineArt is one of the fastest-growing GenAI companies in the world, having scaled without outside funding and developed an image generation model ranked #3 globally for photo realism. They are seeking an Agent Infrastructure Engineer to own Superagent, their core agent harness that powers conversations, tool calls, and multi-step agentic workflows across their AI products. This is a deep systems and infrastructure role focused on the core orchestration loop, tool-calling infrastructure, context and memory management, streaming, retries, evaluation, observability, and performance, rather than prompt engineering or wrapping model APIs.
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.
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 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
- 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.
Additional details
- 100M+ social impressions
- Built and shipped our own image generation model, now ranked #3 globally for photo realism
- No funding. No shortcuts.
- Originally posted on Himalayas