AI Solution Architect

User Experience Researchers Pte Ltd; Singapore; On-site; SGD 150,000 - 210,000; Full time

Job summary

User Experience Researchers Pte Ltd seeks an AI Solutions Architect to lead the integration of Generative AI into workflows. Focused on the Application Layer, you will architect tools for intelligent market analysis, automate decision processes, and bridge LLM capabilities with business systems.

Start as an individual contributor with potential to shape the AI strategy. You will design RAG systems, build autonomous AI agents, and connect LLMs to internal databases and APIs, while exploring

Qualifications

• 3+ years of experience in AI application development with focus on LLMs and NLP.
• Experience building AI-powered workflow automation and production-grade AI systems.
• Familiarity with modern AI tooling and vector databases for scalable analytics.

Responsibilities

• Design and deploy RAG frameworks to transform data into actionable insights for traders and risk teams.
• Develop AI agents and autonomous workflows, moving toward human-in-the-loop automation.
• Build and maintain middleware to connect LLMs with internal databases and APIs.
• Evaluate fine-tuning strategies (PEFT/LoRA) for open-source models when needed.
• Develop a technical AI roadmap identifying high-ROI opportunities and scalable production paths.

Skills

LLMs & NLP

Python

Agentic Workflows

RESTful APIs

PEFT/LoRA

Tools

LangChain

LlamaIndex

Haystack

Pinecone

Milvus

Weaviate

Ollama

vLLM

Job description

We are looking for a pragmatic and results-driven AI Solutions Architect to lead the integration of Generative AI and advanced automation into our department's workflows. Unlike a research-heavy role, this position focuses on the Application Layer: building high-impact tools for intelligent market analysis, automating complex decision-making processes, and architecting the roadmap for our AI evolution.You will be the bridge between cutting-edge LLM capabilities and practical business efficiency, starting as a key individual contributor with the potential to shape our future AI strategy.

Key Responsibilities:

• Intelligent Analysis Systems: Design and deploy RAG (Retrieval-Augmented Generation) frameworks to transform internal reports, market news, and unstructured data into actionable insights for the trading and risk teams.
• Workflow Automation: Develop AI Agents and autonomous workflows to streamline repetitive high-value tasks, moving from manual processes to "human-in-the-loop" automated systems.
• AI Application Development: Build and maintain the middleware/orchestration layer (using frameworks like LangChain or LlamaIndex) to connect LLMs with our internal databases and APIs.
• Model Optimization: Evaluate and implement fine-tuning strategies for open-source models (e.g., Llama 3, DeepSeek) when off-the-shelf APIs do not meet specific accuracy or privacy requirements.
• Strategic Roadmap: Develop a technical AI roadmap that identifies high-ROI opportunities within the department and outlines the transition from pilot projects to scalable production systems.

Technical Qualifications:

• Core AI Engineering: 3+ years of experience in AI application development with a strong focus on Large Language Models (LLMs) and Natural Language Processing (NLP).
• The Orchestration Stack: Proficiency in Python and deep experience with orchestration frameworks such as LangChain, LlamaIndex, or Haystack.
• Automation Mastery: Proven track record of building Agentic Workflows (e.g., AutoGPT, CrewAI) and integrating them into production environments.
• Data & Infrastructure: Familiarity with Vector Databases (e.g., Pinecone, Milvus, Weaviate) and experience handling API integrations (RESTful, GraphQL).
• Model Layer (Bonus): Practical experience in Parameter-Efficient Fine-Tuning (PEFT/LoRA) and local model deployment (Ollama, vLLM).

Preferred Attributes:

• Strategic Thinking: Ability to distinguish between AI "hype" and actual business value, prioritizing projects based on feasibility and impact.
• Collaborative Mindset: Effective at working across teams (IT, Risk, Trading) to understand domain-specific pain points.
• Self-Starter: Comfortable working independently to build prototypes while maintaining a vision for long-term scalability.