AI Solutions Architect (AI Operations & Analytics), Singapore

Role Overview 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.