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.