New opportunity

Associate AI Developer (Part Time, Short Term Contract)

Ng Teng Fong General Hospital · Singapore

About this role

The Associate AI Developer will join the AI Core team at Ng Teng Fong General Hospital to support the Project Metatarsal initiative. The primary objective is to design, develop, and deliver a digital solution that consolidates and operationalizes clinical workflows, patient education materials, and care pathway content for orthopaedic services. Responsibilities include building application components, data pipelines, and integrations to structure clinical content. The role involves embedding AI capabilities such as intelligent search, retrieval, summarization, and conversational interfaces to improve knowledge access and workflow efficiency. The candidate will collaborate with clinical, operational, and transformation stakeholders to translate requirements into scalable, secure, and user-friendly solutions while contributing to reusable AI-enabled capabilities for broader clinical use cases. Requirements: Strong hands-on experience in software engineering using Python, JavaScript, or TypeScript; experience with applied AI/ML or LLM frameworks including retrieval, embeddings, and conversational agents; ability to design and implement APIs, data pipelines, and system integrations; experience working with structured and unstructured data in content-rich environments; familiarity with cloud platforms and deployment practices such as Azure; strong problem-solving skills and the ability to prototype and iterate quickly; and the ability to collaborate with cross-functional stakeholders. Preferred Qualifications: Experience in healthcare, workflow applications, or knowledge management systems is considered advantageous.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonJavaScriptTypeScriptSoftware EngineeringLLM FrameworksRetrievalEmbeddingsConversational AgentsAPI DesignData PipelinesSystem IntegrationCloud PlatformsAzure

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Operationalizing Clinical Content
The core mandate is to consolidate orthopaedic clinical workflows and patient education materials into a functional digital solution. You must prioritize structuring unstructured clinical data into pipelines that support intelligent search and retrieval, ensuring the output is usable for both clinicians and patients.
Embedding AI Capabilities
Beyond basic software development, you are expected to integrate LLM-based features such as summarization and conversational interfaces. This requires a focus on retrieval-augmented generation (RAG) patterns to ensure the AI provides accurate, context-aware responses based on the specific clinical content provided.
Cross-Functional Stakeholder Collaboration
You must translate complex clinical and operational requirements into technical specifications. Success depends on your ability to iterate quickly based on feedback from non-technical stakeholders while maintaining a focus on scalable and secure system architecture.

Likely challenges to think through

Data Complexity and Security in Healthcare
Working with clinical content involves handling sensitive information and unstructured data formats. Prepare by reviewing best practices for data sanitization, secure API design, and ensuring that AI-driven retrieval systems comply with healthcare data privacy standards.
Balancing Prototyping with Scalability
The role requires rapid iteration for Project Metatarsal while simultaneously building reusable capabilities for the broader AI Core team. The challenge is to avoid creating 'throwaway' code; prepare by focusing on modular architecture and documentation that allows your components to be extended to future clinical use cases.

Practice your answers

Technical practice

Implementing RAG for Clinical Content

What to coverExplain your approach to building a Retrieval-Augmented Generation (RAG) pipeline for orthopaedic clinical content. Discuss how you handle document chunking, embedding models, and vector database selection to ensure accurate retrieval of patient education materials.

Data Pipeline Design for Unstructured Clinical Data

What to coverDescribe how you would design a data pipeline to ingest and structure unstructured clinical notes or care pathways. Focus on data cleaning, normalization, and ensuring the output is suitable for downstream AI consumption.

Conversational Agent Architecture

What to coverDiscuss the trade-offs between using a pre-built conversational framework versus a custom implementation for a clinical interface. Address how you would manage context, state, and safety guardrails when interacting with sensitive medical information.

API Design for Scalable AI Services

What to coverHow do you design secure and scalable APIs to expose AI capabilities to front-end applications? Suggest best practices for authentication, rate limiting, and handling asynchronous requests in a clinical environment.

Cloud Deployment on Azure

What to coverDescribe your experience with Azure services relevant to AI development, such as Azure OpenAI, Azure Functions, or App Service. Explain how you would manage deployment cycles and ensure the solution remains secure and compliant.

Evaluating AI Model Performance

What to coverHow do you measure the effectiveness of an intelligent search or summarization tool in a clinical setting? Suggest metrics beyond standard accuracy, such as retrieval relevance, latency, and user feedback loops.

System Integration Challenges

What to coverWhen integrating new AI components into existing hospital workflows, what are the primary technical risks? Discuss strategies for ensuring interoperability with legacy systems while maintaining data integrity.

Prototyping and Iteration Strategy

What to coverGiven the need to iterate quickly for Project Metatarsal, describe your workflow for moving from a proof-of-concept to a functional prototype. Suggest how you balance rapid development with the need for a secure and stable codebase.

Behavioural practice

Translating Clinical Requirements to Technical Solutions

What to coverDescribe a time when you had to work with non-technical stakeholders to define project requirements. How did you ensure their clinical needs were accurately captured and translated into a technical specification?

Managing Conflicting Stakeholder Priorities

What to coverProvide an example of a situation where clinical, operational, and technical stakeholders had conflicting priorities. How did you facilitate a resolution that kept the project on track?

Handling Sensitive Data and Privacy

What to coverDescribe a project where you had to implement strict data security or privacy measures. How did you balance the need for system usability with the requirement to protect sensitive information?

Adapting to Rapidly Changing Project Needs

What to coverTell me about a time when project requirements changed mid-development. How did you adjust your technical approach and communicate the impact of these changes to your team?

Sharpen your knowledge

Topics to prepare

Retrieval-Augmented Generation (RAG) Architecture
Review the conceptual flow of RAG systems, specifically how to ingest unstructured clinical documents, generate embeddings, and store them in a vector database for intelligent retrieval. This is essential for the Project Metatarsal initiative to surface orthopaedic care pathways and patient education materials.
Clinical Workflow Integration
Study the principles of mapping clinical processes into digital workflows. Focus on how to translate complex, multidisciplinary orthopaedic requirements into technical system logic, ensuring the solution is user-friendly for both clinicians and patients.

Skills to revise

LLM Framework Implementation
Practice building a prototype using frameworks like LangChain or LlamaIndex to create a conversational interface. Focus on implementing retrieval and summarization modules, as these are explicitly required to enhance knowledge access in the hospital environment.
Data Pipeline Development
Develop a script to clean and structure unstructured clinical text data into a format suitable for vectorization. This addresses the requirement to handle content-rich environments and prepare data for AI-enabled search capabilities.
Azure Cloud Deployment
Familiarize yourself with Azure-specific AI services, such as Azure OpenAI Service or Azure AI Search. Practice deploying a simple API endpoint that serves a model, reflecting the need for cloud-based deployment practices in a healthcare setting.

Your preparation checklist

Prepare a RAG-based project demonstration
Develop a small-scale prototype using Python or TypeScript that demonstrates Retrieval-Augmented Generation (RAG). Focus on ingesting unstructured clinical or educational documents, creating embeddings, and implementing a search interface to retrieve relevant information, as this directly mirrors the Project Metatarsal requirements.
Map technical skills to clinical workflow challenges
Create a summary of how you have previously built data pipelines or API integrations to handle unstructured data. Be prepared to explain how you would ensure these systems remain secure and scalable when applied to sensitive orthopaedic care pathway content.
Review Azure cloud and deployment practices
Refresh your knowledge on Azure-specific AI services and deployment workflows. Prepare to discuss how you would deploy a conversational interface or search tool within a hospital's cloud environment, keeping in mind the need for rapid prototyping and iteration.
Formulate questions for clinical and operational stakeholders
Prepare thoughtful questions regarding the current state of orthopaedic clinical content, such as how the team currently manages unstructured patient education materials and what specific pain points clinicians face when accessing care pathway information.
Draft a strategy for reusable AI components
Outline a modular approach for building AI capabilities that can be extended beyond orthopaedics. Be ready to explain your philosophy on designing reusable code and APIs that allow the AI Core team to scale solutions to other hospital departments.

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