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TOKEN2049

Head of AI

Hong Kong +1

Location
Hong Kong/Singapore
Work
Not stated
Employment
Not stated
Level
Director
Function
Product
Pay
Not stated
Posted
4 months ago

About the role

Role Overview

We are seeking a visionary and execution-focused Head of AI (Agentic Outreach) to spearhead the evolution of our growth, sales, and engagement marketing engines. In this role, you will design, build, and scale an ecosystem of autonomous AI agents capable of hyper-personalized, context-aware, and multi-channel outreach.

You will bridge the gap between advanced machine learning engineering and strategic business growth, moving beyond simple template automation to build dynamic, self-learning agentic workflows that orchestrate complex B2B/B2C communication strategies.

Key Responsibilities

1. Strategy & Architecture

  • Vision & Roadmap: Define the strategic roadmap for agentic outreach, transitioning from static sequence automation to autonomous, goal-driven AI agent networks.
  • System Architecture: Design and oversee the infrastructure for multi-agent systems that handle lead sourcing, intent signaling, content generation, and multi-turn conversational follow-ups.
  • Guardrails & Ethics: Establish strict compliance, data privacy (GDPR/CCPA), brand voice alignment, and safety guardrails to ensure AI agents interact ethically and accurately.

2. Technical Execution & AI Engineering

  • Agent Orchestration: Build and optimize workflows using framework stacks like LangChain, CrewAI, AutoGen, or custom-built internal orchestration layers.
  • RAG & Memory Systems: Implement advanced Retrieval-Augmented Generation (RAG) and persistent memory vector databases so agents maintain deep context across long-term buyer journeys.
  • Model Optimization: Fine-tune, prompt-engineer, and select the optimal mix of proprietary (OpenAI, Anthropic) and open-source (Llama, Mistral) LLMs to balance cost, latency, and performance.

3. GTM Integration & Analytics

  • Data Pipeline Integration: Connect agentic workflows seamlessly with our modern data stack, CRM (Salesforce/HubSpot), sales engagement tools, and data enrichment APIs (e.g., Clay, Apollo, ZoomInfo).
  • Loop Optimization: Build reinforcement learning loops based on positive/negative conversion signals (e.g., booking a demo vs. "unsubscribe" requests) to continuously improve agent persuasion and timing.
  • KPI Tracking: Own the performance metrics of AI-driven outreach, including pipeline generated, response quality scores, deliverability rates, and ROI.

Qualifications & Skills

Technical Requirements

  • AI/ML Expertise: 5+ years and above of experience working with Large Language Models (LLMs), prompt engineering, and embeddings.
  • Agentic Frameworks: Proven hands-on experience building autonomous agent architectures, multi-agent frameworks, and tool-use (function calling) loops.
  • Programming Proficiency: Expert-level Python developer with experience in AI libraries and vector databases (Pinecone, Milvus, Qdrant, etc.).
  • Data & API Integration: Deep understanding of web scraping, reverse APIs, webhook orchestration, and complex data enrichment pipelines.

Leadership & Strategic Skills

  • Growth Mindset: Strong understanding of modern outbound B2B marketing, sales development (SDR/BDR) playbooks, and growth hacking principles.
  • Team Building: Experience leading and mentoring cross-functional teams of AI engineers, data scientists, and growth marketers.
  • Analytical Rigor: Strong statistical background to design and evaluate rigorous A/B testing frameworks for agentic variations.

Preferred Experience

  • Experience in a fast-paced B2B SaaS environment or a high-growth tech startup.
  • A portfolio of shipped AI products, open-source contributions to agentic frameworks, or public writing/research on the future of autonomous systems.
  • Familiarity with sophisticated email deliverability infrastructure (domain warming, spintax, rotating IPs).

Source

Posted by TOKEN2049 on their own hiring page, job-boards.anz.greenhouse.io. OptiTalent shows it as published, read 07 Nov 2026 01:33 UTC; the original is the reference.

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