Head of AI & Agentic Automation

Position: Head of AI & Agentic Automation Reporting Line: Chief Technology Officer (CTO) Key Stakeholders: VP Engineering Location: China Job Type: Full-time About Elife: What are we building? Elife is not just a company operating a cross-border ride-hailing aggregation platform. We are the /"digital infrastructure/" — the utilities layer — of global mobility. We provide the underlying cross-border capacity routing protocol for the world's leading super apps (such as Didi, Meituan, Transsion, Alipay). With an ultra-light asset model, we enable these giants to instantly access and fulfill ride-hailing and delivery services across 182 countries in seconds — including India's rapidly expanding mobility and delivery ecosystem. Your Core Mission: Not building /"hands and feet,/" but reconstructing an /"all-knowing brain/" While the industry is focused on large models, 90% of efforts are superficial — wrapping GPT-like tools around business use cases to automate responses. That's not what we are building. Your mission is to construct the true /"central brain/" of Elife. You will not operate within a marginalized /"AI innovation lab./" Your battlefield is on the main road: working shoulder-to-shoulder with the VP Engineering, embedding your AI deployment strike team directly into our core agile product and engineering pipeline. Your single objective: fully integrate large models with Elife's core transaction engine — including personalized dynamic pricing, global capacity dispatch systems, and core order allocation logic — transforming traditionally hard-coded systems into a fully AI-native and agent-ready architecture. Execution Focus & Key Responsibilities Architect the Brain (LLM Orchestration Infrastructure): Deeply collaborate with the VP Engineering to establish rigorous API contracts and tool-calling/function-calling mechanisms. Open up core backend capabilities to AI systems, enabling agents not only to understand natural language but to natively interact with real-time global GPS data, dynamically adjust pricing, and directly issue dispatch commands in the physical world. Embed & Synergize with Product and Engineering: Lead a highly specialized AI deployment squad, deeply integrating into existing agile development workflows. Bridge the gap between cutting-edge AI algorithms and traditional backend systems (Java/Go), ensuring smooth AI capability deployment without compromising core system stability. Experience navigating India's large, distributed engineering teams is a strong advantage. End-to-End /"Tokenization/" of Business Workflows: Once the foundational AI infrastructure is in place, provide front-line operations teams (customer service, supplier integrations, etc.) with low-code agent-building frameworks. Enable them to build automated pipelines safely and controllably, replacing costly manual operations with highly efficient token-based compute — with sensitivity to India's diverse operational contexts and multilingual requirements. Elevate AI Engineering Culture: As a core member of the CTO's think tank, lead an internal transformation of engineering culture. Drive adoption of AI-assisted coding tools (such as Cursor / Copilot) across the organization, pushing developers to evolve from manual CRUD coding to prompt engineering and system orchestration, achieving exponential productivity gains. Who We're Looking For (Candidate Profile) A Business-Savvy Technical Veteran: Experienced in high-concurrency systems and microservices, with the emotional intelligence and technical depth to align cross-functional stakeholders in complex organizations. Familiarity with India's regulatory environment — including DPDP Act (Digital Personal Data Protection), RBI data localisation guidelines, and cross-border data transfer compliance — is a strong plus. A bridge-builder — not just a trench fighter. An LLM Engineering Expert: Strong intuition for large language model behavior, with hands-on experience in frameworks like LangChain, LlamaIndex, or similar orchestration systems, as well as RAG architectures. Critical requirement: deep expertise in tool-calling, including production-grade safeguards to ensure AI systems operate reliably and safely — no uncontrolled behavior in production databases. A Systems Thinker: Fundamentally believes that the intelligence ceiling of an agent is defined by the depth of APIs it can access. Deep understanding of high-concurrency scheduling, SLA reliability, and cross-border data compliance challenges — including India-specific data sovereignty considerations. A Results-Driven Operator: Not a /"PowerPoint architect./" Comfortable diving into code, conducting code reviews, and leading execution. Capable of defining top-level strategy while also delivering real, measurable impact — especially through cost reduction and operational efficiency — by closing the loop and getting agents into production.