AI Engineer

About Hytech

Hytech is a premier management consulting firm with headquarters in Australia and Singapore, dedicated to driving digital transformation within fintech and financial services sectors. We offer comprehensive consulting and robust middle- and back-office solutions aimed at helping clients optimize their operations, boost efficiency, and stay competitive in an ever-evolving digital environment. Our clientele includes some of the world's top trading platforms and prominent cryptocurrency exchanges.

Boasting a global workforce exceeding 2,000 professionals, Hytech expands its footprint across countries including Australia, Singapore, Malaysia, Taiwan, the Philippines, Thailand, Morocco, Cyprus, Dubai, and more.

Role Overview

We seek a proactive AI Engineer specialized in designing, developing, and deploying impactful AI solutions tailored for our Customer Service ecosystem. This role emphasizes practical implementation over pure research or theoretical model training. The successful candidate will collaborate with teams in Customer Service, Automation, CRM, and Platform to pinpoint operational challenges and transform them into deployable AI applications. The focus areas include creating AI Agents, leveraging LLM-driven workflows, building retrieval-augmented generation (RAG) knowledge systems, enabling intelligent decision-making, and integrating AI-driven automation with existing applications like Zendesk, CRM, APIs, and automation frameworks.

The candidate must be capable of independently carrying AI projects through all phases: from identifying business challenges to solution design, prototyping, integration, assessment, full-scale deployment, and continuous enhancement.

Key Responsibilities

• Design and implement AI Agents aimed at Customer Service and operational workflows.
• Develop agentic workflows supporting reasoning, decision-making, utilization of tools, and complex multi-step task execution.
• Enable AI Agents to interface effectively with internal systems through APIs and automation tools.
• Incorporate human-in-the-loop mechanisms and exception handling for risk-sensitive scenarios.
• Build and deploy production-quality applications utilizing commercial or open-source large language models.
• Execute prompt engineering, structured response formatting, function/tool invocation, context management, and LLM orchestration.
• Create AI-based solutions for tasks such as classification, summarization, information extraction, decision support, response generation, and workflow automation.
• Assess diverse models by criteria including accuracy, latency, cost efficiency, security, and business suitability.
• Design and enhance retrieval-augmented generation (RAG) systems to handle structured and unstructured customer service knowledge such as SOPs, policies, and FAQs.
• Improve retrieval accuracy, grounding, citations, chunking strategies, metadata management, and overall knowledge fidelity.
• Develop evaluation frameworks to continuously measure AI response quality and highlight knowledge deficiencies.
• Advance customer intent classification and routing capabilities, incorporating intent, conversational context, business rules, and system data.
• Facilitate intelligent transfer processes between AI agents, automation platforms, and human representatives.
• Establish confidence thresholds and eligibility criteria to govern safe AI or automated interventions.
• Collaborate extensively with automation engineers to merge AI decision logic with workflows, APIs, and automation channels.
• Integrate AI functionalities with CRM platforms, Zendesk, internal technology stacks, and other operational systems.
• Create reusable tools that empower AI Agents to access information or trigger authorized operations.
• Support advancement from traditional rule-based automation toward AI-triggered and autonomous agentic automation.
• Construct systematic evaluation methodologies covering metrics such as accuracy, hallucination mitigation, retrieval performance, intent precision, task completion rates, and business outcomes.
• Support AI-powered quality control initiatives including conversation analysis, compliance verification, root-cause diagnostics, and insight generation.
• Implement monitoring and feedback frameworks to perpetually enhance AI solution performance in production environments.
• Drive the end-to-end AI project lifecycle: spotting operational AI opportunities, translating requirements, rapid prototyping, validating business impact, production deployment, and sustained optimization.
• Engage directly with Customer Service stakeholders to understand and address their evolving technical needs.

Candidate Requirements

• Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related discipline.
• Minimum three years of professional experience in software engineering, AI/ML, or applied AI development, with tangible involvement in building AI-driven applications.
• Proficient programming skills in Python.
• Practical experience with large language model APIs and/or open-source LLM frameworks.
• Skilled in prompt engineering, retrieval-augmented generation (RAG), embedding/vector search methodologies, structured outputs, function/tool calling, and evaluating LLM performance.
• Strong grasp of RESTful APIs, data interchange formats like JSON, authentication protocols, and system integration principles.
• Experience developing backend services or AI applications using frameworks such as FastAPI or similar technologies.
• Demonstrated ability to independently progress technical solutions from prototype stages to full production deployment.
• Exceptional problem-solving capabilities combined with a robust understanding of business workflows.
• Comfortable interacting and collaborating directly with business and operational teams.