AI Engineer

Role Summary

UltreonAI is an AI Design Studio, at the forefront of designing and deploying custom Agentic AI solutions specifically tailored to enhance the efficiency and competitiveness of Singaporean SMEs. Our mission is to move businesses beyond traditional systems by implementing smart, context-aware AI agents.

This role offers a unique opportunity to be a foundational part of this mission. The AI Engineer will be responsible for architecting and implementing the intelligence layer of our custom AI solutions. You will design AI systems that go beyond simple API calls-building sophisticated RAG architectures, orchestrating multi-step agent workflows, and ensuring AI outputs are reliable, cost-effective, and aligned with client business requirements.

Key Responsibilities

Your primary focus will be on designing, implementing, and optimizing the AI systems that power our client solutions.

• AI System Architecture: Design and implement complete AI systems-not just API integrations, but thoughtful architectures that handle retrieval, reasoning, tool execution, and response generation in production environments.
• RAG Pipeline Development: Build and optimize Retrieval-Augmented Generation systems including document processing, chunking strategies, embedding generation, vector search optimization, and context assembly for LLM prompts.
• Prompt Engineering: Develop robust, structured prompts that reliably produce desired outputs across diverse use cases. Implement prompt templates, few-shot examples, and structured output schemas to ensure consistent AI behavior.
• Agent Orchestration: Design multi-step agentic workflows where AI agents make decisions, execute tools (database queries, API calls, web searches), manage state, and handle complex business logic autonomously.
• Quality Assurance & Evaluation: Establish evaluation frameworks to measure AI output quality, identify failure modes, detect hallucinations, and implement mitigation strategies. Monitor system performance and iterate on improvements.
• Cost & Performance Optimization: Manage token usage, implement caching strategies, optimize context windows, and balance quality vs cost tradeoffs across different LLM models and deployment strategies.

Technical Skills & Qualifications

Required Technical Competencies

• AI System Design: Deep understanding of how to architect AI systems-not just calling LLM APIs, but designing complete pipelines including retrieval, context preparation, prompt orchestration, output parsing, and error handling.
• RAG Architecture: Hands-on experience building Retrieval-Augmented Generation systems. Understanding of chunking strategies, embedding models, vector similarity search, hybrid search approaches, and context ranking/reranking techniques.
• Prompt Engineering: Expert-level prompt engineering skills-structured outputs (JSON mode, function calling), chain-of-thought prompting, few-shot learning, system message design, and prompt optimization for reliability and cost.
• Agent Orchestration: Experience designing agentic workflows where AI systems make multi-step decisions, use tools, maintain conversation state, and handle complex reasoning tasks. Familiarity with frameworks like LangChain, LlamaIndex, or custom orchestration logic.
• TypeScript & Node.js: Strong proficiency in TypeScript for AI integration work. Comfortable building AI pipelines in Node.js environments and integrating with backend APIs.
• LLM APIs: Deep familiarity with LLM APIs (OpenAI, Anthropic, or equivalent)-streaming responses, function calling, token management, error handling, rate limiting, and API cost optimization.
• Vector Databases: Hands-on experience with vector databases (Pinecone, Weaviate, ChromaDB) for semantic search, including index optimization, metadata filtering, and hybrid search implementations.
• Redis: Understanding of using Redis for AI context caching, conversation memory, rate limiting, and managing stateful agent interactions.
• Evaluation & Quality Control: Ability to design evaluation frameworks-measuring accuracy, relevance, coherence, and detecting failure modes like hallucinations or off-topic responses.

Data & Integration Skills

• Data Preparation: Working with Data Engineers to prepare client data for AI pipelines-understanding data quality requirements, preprocessing needs, and how data structure affects AI performance.
• Backend Integration: Collaborating with Backend Engineers to integrate AI systems into production APIs-handling async workflows, managing long-running agent tasks, and ensuring reliable error handling.
• System Constraints: Understanding backend constraints like API latency, token limits, cost budgets, and designing AI systems that respect these boundaries while delivering value.

Good to Have

• Multi-Language Support: Experience building AI systems that handle multiple languages-understanding tokenization differences, cross-lingual embeddings, and localization challenges.
• AI Safety & Mitigation: Knowledge of hallucination detection techniques, content filtering, bias mitigation strategies, and implementing guardrails for safe AI outputs.
• Fine-Tuning & Model Optimization: Experience fine-tuning models or working with open-source LLMs for specialized use cases or cost optimization.
• Advanced Retrieval Techniques: Familiarity with hybrid search (dense + sparse), query expansion, parent-child chunking, or other advanced retrieval patterns.

Education & Background

• Currently pursuing or recently graduated with a Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, or a related technical field.

Benefits / What You'll Gain

This role is designed to be a high-impact, skill-accelerating experience:

• Full AI Solution Lifecycle Exposure: Gain practical, end-to-end experience architecting and deploying AI systems in production-moving beyond tutorials and toy projects to live systems serving real business needs.
• Cutting-Edge AI Technologies: Hands-on work with the latest AI technologies-RAG architectures, agentic workflows, vector databases, prompt engineering at scale-skills that are defining the future of AI engineering.
• Real-World Impact: Directly contribute to the success of live Singapore SME projects, building AI systems that solve tangible business problems and deliver measurable value to clients.
• System Design Ownership: Take ownership of AI architecture decisions-you're not just implementing someone else's design, but architecting intelligent systems from the ground up.
• Mentorship: Direct collaboration and guidance from the Tech Lead, providing deep insight into AI system architecture, production best practices, and tradeoffs in designing reliable AI solutions.
• AI Engineering Mastery: Develop specialized expertise in building production AI systems-a highly sought-after skill set as companies move from AI experimentation to AI deployment.

Application Details

• Work Arrangement: This is a full/part time hybrid working position with flexible working hours. Occasional in-person collaboration in Singapore as needed for key project milestones.
• Probation Period: 6-month probation period to ensure mutual fit and alignment.
• Team Structure: You'll work in project-based teams rather than siloed departments, collaborating closely with Backend Developers, Data Engineers, and Frontend Developers to integrate AI capabilities into complete solutions.

Join us to architect the AI systems that power the next generation of business intelligence and Agentic AI for Singapore SMEs!