Research Analyst/ Engineer: AI/ML (Ophthalmology) - Contract

Job Description:

• Hybrid Architecture Development: Responsible for designing the communication architecture between the client (Windows) and the cloud or local backend, managing complex business logic and system workflows.
• AI Model Integration: Deploy and optimize Automated Speech Recognition (ASR) models to accurately recognize various accents (e.g., Singaporean) , and utilize Computer Vision to monitor patient posture and ensure the proper use of the eye occluder.
• Multi-display Interaction Design: Orchestrate real-time, event-driven state machines to ensure seamless state synchronization across multiple displays, specifically the patient-facing touchscreen and the Snellen chart monitor placed at a 4-meter distance.
• Hardware Coordination: Integrate and maintain data links and drivers for various peripherals, including NRIC scanners, thermal printers, high-resolution webcams, and microphones.
• Data Security & Compliance: Ensure secure storage of patient test results and performance logs in the cloud (Amazon S3) , implementing strict data anonymization to prevent the retention of sensitive personal information.

Job Requirements:

1. Education & Background

• Studied/Studying in Computer Science, Software Engineering, Computer Engineering, or a related technical field.
• Strong interest in learning analytical and problem-solving skills
• Ability to work in a fast-paced environment and manage multiple priorities

2. Front-end Development

• JavaScript Development & Engineering: Solid proficiency in JavaScript (ES6+) with the ability to manage application logic independently. Familiar with modular development and modern build tools (e.g., Webpack, Vite).
• Real-time Media APIs: Extensive experience with browser-based media handling, specifically MediaDevices for camera/microphone access and WebRTC/WebSocket for low-latency streaming.
• Client-side AI & WASM: Familiarity with ONNX Runtime Web to implement browser-side Voice Activity Detection (VAD).
• Hybrid Rendering: Experience with server-side templating engines (e.g., Jinja2, EJS, or similar). Ability to bridge the gap between server-rendered content and dynamic client-side updates via Vanilla JS and DOM manipulation.
• Hardware Integration: Experience integrating third-party SDKs (e.g., thermal printer SDKs) into web-based workflows.

3. Back-end & AI Engineering

• Python Engineering: Expert in modern Python development. Skilled in building robust backend services and managing application logic with a focus on code quality and efficiency.
• Audio & ASR Inference: Practical experience in deploying and optimizing mainstream ASR frameworks (e.g., Whisper, FunASR, or similar). Familiar with offline ASR workflows and capable of tuning inference performance for specific use cases.
• Computer Vision (CV):
• Hands-on experience with MediaPipe for facial landmark, iris detection, and contour extraction.
• Proficiency in OpenCV for specialized tasks like ArUco marker detection and adaptive image preprocessing.
• State Management & Synchronization: Expertise in designing event-driven state machines. Proven ability to ensure synchronized state across multiple displays (e.g., Touchscreen vs. VA Display) using tools such as Redis, WebSockets, or other real-time communication protocols.

4. AWS for Data Storage:

• Practical experience with Amazon S3 for secure data persistence.
• Proficiency in Boto3 for backend data uploading and AWS SDK (v3) for frontend data retrieval.
• Familiar with IAM roles and policies to ensure secure access to patient data.

5. AWS Skills for Cloud Computing

• Amazon Kinesis Video Streams (WebRTC): Hands-on experience building and troubleshooting KVS WebRTC pipelines, including Signaling Channels, role separation (MASTER/VIEWER), and signaling workflows (SDP offer/answer, ICE candidates).

• Cloud Integration: Skilled in AWS SDKs (Boto3, JS v3) and IAM security best practices (Credential management, secure signaling).
• Operational Awareness: Comfortable managing region/config via environment variables and deployment settings (e.g., AWS_REGION, credentials, proxy config), and interpreting AWS-side failures from logs/metrics for rapid root-cause analysis.