About Hytech

Hytech is a leading management consulting firm headquartered in Australia and Singapore, specialising in digital transformation for fintech and financial services organisations. The company delivers end-to-end consulting services and provides robust middle- and back-office solutions that enable clients to optimise operations, enhance efficiency, and stay ahead in a fast-evolving digital landscape. Hytech’s client portfolio includes top global trading platforms and leading crypto exchanges.

With more than 2,000 professionals worldwide, Hytech has a strong and growing international presence, with offices across Australia, Singapore, Malaysia, Taiwan, the Philippines, Thailand, Morocco, Cyprus, Dubai, and beyond.

Role Overview

We are seeking a hands-on AI Engineer to design, build, and deploy practical AI solutions within our Customer Service ecosystem. This role is focused on delivering production-ready AI solutions, not research or purely model-training. The successful candidate will collaborate closely with Customer Service, Automation, CRM, and Platform teams to identify operational pain points and translate them into effective AI solutions. Key responsibilities include building AI Agents, LLM-powered workflows, knowledge/RAG solutions, intelligent decisioning, and AI-driven automation, with integration into platforms such as Zendesk, CRM systems, APIs, and Automation solutions. The ideal candidate will independently manage AI use cases from business problem identification through to continuous improvement in production.

Key Responsibilities

AI Agent & Agentic Workflow Development
- Design and develop AI Agents for Customer Service and operational use cases.
- Build agentic workflows involving reasoning, decision-making, tool calling, and multi-step task execution.
- Enable AI Agents to interact with internal systems through APIs, automation tools, and other integration mechanisms.
- Design human-in-the-loop and exception-handling mechanisms for higher-risk scenarios.

LLM & Applied AI Development
- Develop production-ready applications using commercial and/or open-source LLMs.
- Implement prompt engineering, structured outputs, function/tool calling, context management, and LLM orchestration.
- Design AI solutions for classification, summarization, information extraction, decision support, response generation, and workflow automation.
- Evaluate different models and approaches based on accuracy, latency, cost, security, and business requirements.

Knowledge & RAG
- Design and improve RAG-based knowledge retrieval systems.
- Work with structured and unstructured Customer Service knowledge, SOPs, policies, and FAQs.
- Improve retrieval quality, grounding, citation, chunking, metadata, and knowledge accuracy.
- Develop evaluation framewor