AI Engineer

Responsibilities

• Architect the next-generation execution runtime (Agent Harness) that drives SuperApp’s planning, reasoning, memory retention, and tool-execution loops. Build reliable state machines capable of pausing, resuming, and versioning agent trajectories.
• Design and scale highly secure, isolated, and ephemeral environments (using Docker, gVisor, WebAssembly, or microVMs) to execute agent-generated code safely without putting host infrastructure at risk.
• Build and maintain high-throughput APIs and integration layers (such as the Model Context Protocol - MCP) that connect SuperApp to external services, databases, web search utilities, and complex file processors.
• Engineer robust system-level guardrails to detect and mitigate prompt injection, defend against jailbreak attempts, and ensure strict compliance with system prompt instructions and data isolation boundaries.
• Implement advanced routing, context window pruning, and context caching strategies to optimize LLM token usage, minimize round-trip latencies, and drastically reduce the operational cost of complex agent loops.
• Write comprehensive high-level design documents, align cross-functional engineering teams on the technical roadmap, and mentor senior and mid-level software engineers across the organization.

Requirements

• Minimum 8+ years of professional software engineering experience, with a proven track record of designing, scaling, and operating mission-critical distributed systems.
• 2+ years of production experience building or modifying agent execution environments, LLM orchestrators, or tool-calling frameworks (e.g., custom runtimes, LangChain, AutoGen, or similar systems).
• Expert proficiency in Go and Python. Pragmatic, deep understanding of concurrent programming, microservices, gRPC, and highly scalable API design.
• Strong experience with containerization and virtualization technologies (Docker, Kubernetes) and a solid grasp of sandboxing untrusted code execution.
• Deep familiarity with message brokers (e.g., Kafka, RabbitMQ), caching infrastructure (Redis), and relational/non-relational database design.
• Bachelor’s or Master’s degree in Computer Science, engineering, or equivalent practical systems engineering experience.

Nice-to-Haves

• Prior experience developing developer tools, SDKs, or extensible plug-in architectures.
• Experience with frontend integration (React, TypeScript) to support human-in-the-loop debugging interfaces or agent visual builders.
• Experience working in high-growth, fast-paced startup environments.

Compensation

• Base salary range: $230000 to $315000 + bonus, equity, and benefits.