About the job
Assurity Trusted Solutions (ATS), a fully owned subsidiary of the Government Technology Agency (GovTech), has been a reliable partner for over a decade. We provide a diverse range of services including infrastructure, operational, authentication, governance, assurance services, and managed processes. In an ever-evolving digital and cyber landscape, where trust and collaboration are paramount, ATS is committed to fostering collaborative relationships with GovTech, government agencies, and commercial partners to mitigate cyber risks and enhance security measures.
About Us
We specialize in building and managing mission-critical digital products at scale. Our teams collaborate closely with stakeholders to design and implement practical, product-centric solutions that meet genuine operational needs.
AI is integral to our operations—encompassing intelligent automation, advanced search capabilities, large language model (LLM)-powered applications, agentic workflows, and decision-support systems. Our engineers are engaged in a variety of projects: from rapid Proof-of-Concepts (POCs) to shared AI frameworks and production-grade systems.
Operating in a cloud-native environment (AWS/Azure), we primarily utilize Python and JavaScript/TypeScript, and deliver modern web applications through frameworks like React and Vue.js. We prioritize strong engineering principles, practicality, and operational excellence.
Your Role
As a Fullstack AI Engineer, you will:
- Design, develop, and maintain end-to-end AI-powered applications that integrate frontend, backend, and AI components.
- Rapidly create Proof-of-Concepts (POCs) to validate feasibility and showcase value within tight deadlines.
- Enhance core AI frameworks, libraries, and reusable components to facilitate quicker and more consistent AI development across the team.
- Build production-grade AI systems focused on scalability, reliability, security, and maintainability.
- Implement LLM-powered features, including prompt design, orchestration, structured outputs, and evaluation methods.
- Develop agentic or multi-step AI workflows using contemporary AI frameworks (e.g., LangGraph, LangChain).
- Design and integrate search and retrieval systems, covering data ingestion, vector indexing, and hybrid search methodologies.
- Create backend services and APIs utilizing Python-based frameworks (e.g., FastAPI, Django) and/or Node.js/Nest.js.
- Develop user-friendly interfaces using modern frontend frameworks (e.g., React, Vue.js).
- Contribute to LLMOps / MLOps practices to streamline AI workflows.

