Staff Backend AI Platform Engineer

Key Responsibilities

• Lead Implementation: Act as the lead engineer for agent runtime orchestration services, focusing on reasoning, planning, and tool invocation.
• API & SDK Design: Own the development of robust APIs and SDKs that enable internal and external teams to build and deploy agent workflows.
• State & Memory Management: Build and optimize stateful dialog management and memory services for multi-turn, context-aware agents.
• Agent Communication: Implement multi-agent communication (A2A) protocols and shared context systems.

System Architecture & Execution

• Microservices Leadership: Execute the transition to cloud-native, event-driven backend services using microservices or service mesh architectures.
• Workflow Design: Build and manage task scheduling and long-running operations using tools like Temporal or Airflow.
• Performance Optimization: Optimize LLM orchestration for performance and cost through caching, batching, and token monitoring.

Integrations & Data Services

• Data Pipeline Ownership: Implement and maintain RAG pipelines and integrations with vector databases (e.g., Pinecone, Weaviate, FAISS).
• Telemetry Integration: Ensure all backend services support LangSmith-based evaluation and comprehensive observability.

Security & Reliability

• Tenant Isolation: Implement RBAC, API authentication, and multi-tenant isolation logic.
• Engineering Excellence: Set the standard for automated testing (unit, integration, load) and implement OpenTelemetry for full system visibility.
• Mentorship: Provide technical guidance and code reviews for more junior backend engineers on the team.