Lead Engineer, AI Agent Systems

Responsibilities

Architecture Leadership and Evolution

• Lead the architecture and evolution of next-generation agent infrastructure designed for complex, knowledge-intensive work.
• Define clear boundaries and collaboration mechanisms across three core layers: the execution engine, context and reasoning orchestration, and the agent capability foundation. Ensure high availability, reliability, and long-term extensibility in environments with a low tolerance for hallucinations and incorrect outputs.

Agent Execution Engine

• Design and implement the Agent Loop runtime and its middleware pipelines.
• Lead the execution and orchestration of planning and sub-agent workflows, including task decomposition, dependency management, concurrency control, and execution scheduling.
• Build mechanisms for checkpointing, interruption and resumption, failure recovery, self-healing, authorization, and cost control to ensure the reliable execution of long-running and complex multi-step tasks.

Context and Reasoning Orchestration

• Own the design and implementation of core context orchestration capabilities.
• Develop strategies for input standardization, dynamic capability representation, and hierarchical context-budget management, including structured degradation when resource or context limits are reached.
• Build structured task workspaces that support efficient organization of dynamic context. Address challenges including long-history compression, tool-output normalization, evidence traceability, and the management of information across different stages of a task.

Agent Capability Foundation

Lead the development of foundational agent capabilities, including

• Secure sandboxed environments using technologies such as Docker, Kubernetes, and AST-based controls
• Multi-layer memory stores
• Retrieval and knowledge-access capabilities
• An MCP (Model Context Protocol) Hub
• Skill execution and management engines
• File-processing and transfer pipelines
• Multi-tenant isolation and security controls
• End-to-end observability and diagnostics

Technical Leadership and Team Enablement

• Remain hands-on and personally contribute code to critical platform modules.
• Lead technical decomposition, architecture decisions, code reviews, and the development of automated evaluation systems and feedback loops.
• Guide the engineering team in translating specific business use cases into reusable platform and infrastructure capabilities.

Qualifications

Engineering and Leadership Experience

• At least five years of professional software engineering experience.
• Proven experience leading the design and delivery of complex software systems beyond standard CRUD applications or basic integrations with AI APIs.
• Demonstrated experience operating as a Tech Lead, Staff Engineer, or equivalent technical leader.
• Experience leading an engineering team of at least three people.

Core Engineering Capabilities

• Strong Python software-engineering skills and the ability to independently own critical platform modules.
• Deep experience with common engineering challenges such as streaming responses, asynchronous and concurrent execution, and multi-model routing and provider integration.
• Strong judgement in balancing system reliability, security, cost, latency, and delivery speed.
• Solid understanding of distributed systems, production architecture, debugging, and operational reliability.

Depth in AI and Agent Systems

Candidates must have substantial hands-on engineering experience with Agent and LLM systems, with deep expertise in at least two of the following three areas:

Execution Engine

• Multi-step reasoning loops
• Tool lifecycle management
• Planning and sub-agent orchestration
• Interruption and resumption
• Failure recovery and self-healing

Context and Reasoning Orchestration

• Input standardization
• Context assembly
• Context and token-budget governance
• Provider-specific request shaping
• Task-stage modelling
• Long-context compression and evidence traceability

Agent Capability Foundation

• Sandbox isolation
• Memory and retrieval systems
• MCP infrastructure
• File-system and file-processing capabilities
• Multi-tenant isolation
• Security, monitoring, and observability