Agent Infrastructure Engineer

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 .