Staff Engineer, AI & Business Operations

The Company:

Faraday Future (FF) is a California-based mobility company, leveraging the latest technologies and world’s best talent to realize exciting new possibilities in mobility. We’re producing user-centric, technology-first vehicles to establish new paradigms in human-vehicle interaction. We’re not just seeking to change how our cars work – we’re seeking to change the way we drive. At FF, we’re creating something new, something connected, and something with a true global impact.

Your Role:

As Staff Engineer, AI & Business Operations, you will be responsible for building an enterprise-grade AI Agent platform that elevates large model capabilities from an “information generation tool” to “executable business productivity,” driving AI to play a deep role in the company’s core business processes and operational decision-making. You will lead the design and implementation of an enterprise-level Agent system similar to OpenClaw, connecting AI models, enterprise data, and business systems to deliver an end-to-end closed loop: task understanding → data retrieval → decision generation → automated execution. The platform will serve sales and commercial systems and gradually expand to enterprise operations and management scenarios.

Key Responsibilities:

• AI Agent Platform Development: Design and implement an enterprise-grade AI Agent platform (Agent Runtime) supporting task understanding, planning, multi-step execution, and state management (memory). Build a unified Tools Layer that encapsulates enterprise APIs, data services, and operational capabilities, enabling Agents to securely invoke CRM, ERP, data platforms, and other systems. Design multi-Agent and multi-step collaboration mechanisms to support automated execution of complex business workflows.
• Workflow Automation & Orchestration: Based on OpenClaw or similar frameworks, build cross-system task orchestration capabilities to achieve a closed loop from AI-driven insights to business execution. Design secure and controllable execution mechanisms including approval nodes, human-in-the-loop collaboration, and rollback strategies. Drive AI automation in business processes such as customer follow-up, task generation, data updates, and process triggering.
• AI Enablement for Sales & Commercial Systems: Build AI Copilot and Agent capabilities to support sales operations, lead management, and customer engagement. Integrate with CRM, CPQ, order management, and customer service systems to achieve deep fusion of AI and business processes.
• AI Enablement for Enterprise Strategy & Operations: Collaborate with management and operational teams to identify AI application scenarios in revenue growth, conversion optimization, customer operations, cost control, and risk management. Build a Decision Copilot for leadership to support data-driven decision-making. Connect data platforms and AI systems to enable the flow: Data → Insights → Decisions → Execution (via Agent automation). Establish a quantitative evaluation system linking AI outcomes to business KPIs such as revenue, conversion rates, and efficiency gains.
• RAG & Enterprise Knowledge System: Build an enterprise-grade Retrieval-Augmented Generation (RAG) system integrating knowledge bases, business data, and real-time information. Support permission-aware retrieval, citation tracing, result explanation, and temporal sensitivity controls. Optimize embeddings, indexing strategies, reranking, and caching mechanisms to improve accuracy and performance.
• Security, Governance & Production Systems: Design security and governance mechanisms for AI systems, including execution tiering (read-only / low-risk / high-risk operations). Build a comprehensive audit and compliance framework to make AI behavior traceable and explainable. Establish a production-grade evaluation system (offline + online), A/B testing, and continuous optimization processes.
• Observability & Platform Engineering: Build AI system observability capabilities including logging, tracing, and metrics. Monitor model calls, tool execution paths, latency, and costs. Drive platform engineering efforts to standardize and scale AI capabilities across the organization.
• Technical Leadership: As a Staff Engineer, lead system architecture design and key technical decisions. Drive AI capabilities