Technology Engineer (Full Stack)

We are seeking a Technology Engineer to architect and build our internal systems and applications.

This is a dual-track role unlike most engineering positions. You will contribute as a hands-on full stack engineer building our investment platform.

You do not need to be the most senior engineer in the room. But you need to be the most curious one about AI — and the most effective at helping others see and capture its value.

Key Responsibilities

1. Technology Development

• Co-Development: Work alongside internal and the partner vendor’s engineering team, contributing code and Pull Requests (PRs) to ensure seamless integration between vendor deliverables and internal systems.
• Maintenance & SLAs: Manage the vendor during the maintenance phase, ensuring adherence to our Incident Management Service Level Agreements (e.g., ensuring Critical/Severity 1 issues are resolved within 4 hours).
• Full-Stack Engineering: Be part of the technical design and development of our full-stack application platform.
• DevOps & Release Management: Own and improve our CI/CD pipelines (GitHub Actions) as a team — build, test, and deployment automation across dev, staging, and production.

2. AI Adoption

• Stay current with the evolving landscape of AI coding tools, LLM capabilities, and enterprise AI applications
• Open to adoption of AI-assisted development tools (e.g. GitHub Copilot, Cursor, Claude) across the engineering team
• Collaborate with other tech engineers on generative AI initiatives, including prompt engineering, RAG implementations, and other AI-assisted workflows
• Maintain a pragmatic view of AI adoption: balancing enthusiasm for what is possible with a clear-eyed assessment of what is appropriate in a regulated, client-sensitive environment

Requirements

Technical Skills

• 3-5 years of software engineering experience with a solid foundation in full-stack or backend development
• Working proficiency in React.js, TypeScript, Python and Node.js
• Comfort with PostgreSQL, ORM-based data access, and database migration workflows (e.g. Alembic)
• Demonstrated, hands-on experience using AI tools in a software development workflow — not just awareness, but active, daily use (e.g. AI-assisted coding, code review, documentation, test generation)
• Familiarity with LLM concepts and tooling: prompt engineering, tool/function calling, retrieval-augmented generation (RAG), and API integration with providers such as OpenAI, Anthropic, or Azure OpenAI
• Practical familiarity with DevOps practices — building and maintaining CI/CD pipelines (GitHub Actions), containerization (Docker), and infrastructure/environment management on Azure Cloud (App Services, Entra ID, PostgreSQL on Azure).
• Comfortable with basic monitoring/logging and diagnosing issues across the deployment pipeline, even if not formally trained as a DevOps engineer
• An understanding of, or genuine willingness to learn, MAS TRM guidelines and the PDPA considerations relevant to working in a regulated financial services firm

Preferred/Bonus

• Experience facilitating workshops, lunch-and-learns, or structured knowledge-sharing sessions
• Prior experience in fintech, wealth management, or any regulated industry
• Familiarity with vector databases, semantic search, or AI agent frameworks
• Exposure to infrastructure-as-code (e.g. Terraform, Bicep) or observability tooling(e.g. Azure Monitor, Application Insights)

Cultural Fit

• Genuinely excited about AI — and grounded about it. You follow developments in AI closely, experiment regularly, and form your own views. But you also understand that in a wealth management firm serving conservative clients and operating under regulatory oversight, adoption must be thoughtful, not reckless.
• A natural enabler. You get energy from helping others level up. Whether it is a fellow engineer or a client-servicing colleague who has never heard of a prompt, you have the patience and communication skills to meet people where they are and bring them along.
• Curious and self-directed. The AI landscape moves fast. You do not wait to be pointed at the next useful tool — you find it, test it, and bring back what is worth sharing.
• Careful and thorough. You understand that in wealth management, the data you work with is sensitive and the trust you are given is real. You take data handling, access controls, and compliance seriously — not as friction, but as part of doing the job well.