AI Engineer, Computer Vision

Lead strategic AI initiatives: Drive the end-to-end design, training, and deployment of state-of-the-art deep-learning and multimodal models that scale to millions of users — spanning computer vision, vision-LLM, multimodal fusion, and AI-agent applications. Architect scalable models & systems: Build and own production-grade AI pipelines for OCR / text extraction, biometric matching, fraud-pattern recognition, and AIGC generation/detection — engineered for low latency, high availability, and strong reliability. Own face intelligence & liveness: Lead anti-spoofing capabilities against presentation, injection, and DeepFake attacks, and develop image/video quality models that keep security decisions reliable in real-world conditions. Build identity & trust signals: Leverage user behavior, historical patterns, and cross-modal risk indicators to produce robust identity-quality and trust signals. Drive execution & collaboration: Deliver high-quality, measurable results while managing ambiguity and prioritizing technical debt; partner with backend, product, infrastructure, risk, and compliance teams to integrate AI into large-scale systems, and influence stakeholders across technical and non-technical backgrounds. Advance applied R&D: Drive experimentation in vision-LLM, multimodal representation learning, and AI agents; stay current with academic and industry advances and translate them into practical, production-ready defenses. Elevate the technical bar & mentor: Provide technical guidance and mentorship, set a high engineering standard, and democratize applied-AI best practices across the team and the wider organization. Requirements: Prior hands-on experience developing AI/ML solutions in areas such as computer vision, AI agents, multi modal learning, vision-LLM, or generative video/image modeling — including significant experience deploying AI/ML systems into production. Deep AI/ML expertise: Strong proficiency with PyTorch and/or TensorFlow, and solid experience with large-scale production ML systems. Strong technical & coding skills: Strong coding ability (e.g., Python) and the system-level thinking to design end-to-end AI solutions with high availability and low latency. Technical leadership: Demonstrated ability to lead complex, cross-team technical initiatives and deliver impactful AI solutions at scale. Strategic communication: Strong communication and leadership skills, with experience influencing technical direction across engineering, product, and leadership teams. Min. Bachelor's degree or higher in Computer Science, Electrical Engineering, or a related field. For senior role- Mentorship: A proven track record of mentoring senior engineers and raising the technical bar. EA Licence No: 99C4599 EA Personnel Reg No: R1104662 EA Personnel Name: Celine Tan Si Ling We regret to inform that only shortlisted applicants will be notified.