Key Responsibilities
- Model Selection & Optimization: Identify, experiment with, and fine-tune state-of-the-art machine learning models for tasks such as video analytics, object recognition/detection, fraud detection, and multimodal generative AI.
- Deep Learning & LLMs: Work with transformer architectures, foundation models, and generative AI to develop and enhance multimodal AI solutions.
- Computer Vision: Develop and optimize models for real-time object detection, tracking, and recognition in video streams, ensuring performance in diverse conditions.
- Fraud Detection and other anomaly detection: Design and implement machine learning models for anomaly detection and fraud prevention using advanced statistical and AI techniques.
- Data Engineering & Processing: Preprocess large datasets, design efficient pipelines for real-time and batch processing, and integrate multimodal data sources (images, text, audio, video).
- Deployment & Scalability: Deploy models in production using cloud-based or edge computing solutions, ensuring performance, scalability, and cost-efficiency.
- Research & Innovation: Stay updated on the latest AI research, evaluate emerging models, and propose enhancements to improve performance and robustness.
- Collaboration & Integration: Work closely with software engineers, data scientists, and domain experts to integrate AI models into end-user applications.
Requirements
- Degree in Computer Science, AI, Machine Learning, or a related field.
- 4 to 5 years of relevant experience
- Strong experience in deep learning frameworks such as TensorFlow, PyTorch, or JAX.
- Proficiency in computer vision techniques, including object detection (YOLO, Faster R-CNN), video analytics, and multimodal learning.
- Hands-on experience with LLMs, transformers (GPT, BERT, T5, CLIP, etc.), and multimodal AI for text, image, and video synthesis.
- Experience in fraud detection and/or anomaly detection using machine learning, pattern recognition, and risk modeling.
- Experience in AWS SageMaker for model training and deployment preferred.
- Familiarity with MLOps and deployment on cloud platforms (AWS and/or GCP preferred) or edge devices.
- Strong problem-solving and analytical thinking abilities.
- Ability to work in a fast-paced, research-driven environment and adapt to evolving challenges.
- Excellent communication skills for presenting findings and collaborating across teams.
- Experience working with autonomous systems, robotics, or edge AI is a plus.
Interested parties, please click the Apply Now below AND apply via the GO portal.
We regret that only shortlisted applicants would be notified.
Toh Wen Qi, Celeste | REG No : R
PERSOLKELLY SINGAPORE PTE LTD | EA License No : 01C4394
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