Machine Learning / AI Engineer

About the Role

We are seeking a highly skilled Machine Learning / AI Engineer to design, develop, and fine-tune AI models for a range of advanced applications, including video analytics, object recognition and detection, fraud detection, and multimodal generative AI leveraging large language models (LLMs). You will work at the intersection of deep learning, computer vision, and natural language processing, driving innovation and ensuring our models deliver high accuracy and efficiency for real-world deployments.

Key Responsibilities

1. 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.
2. Deep Learning & LLMs: Work with transformer architectures, foundation models, and generative AI to develop and enhance multimodal AI solutions.
3. Computer Vision: Develop and optimize models for real-time object detection, tracking, and recognition in video streams, ensuring performance in diverse conditions.
4. Fraud Detection and other anomaly detection: Design and implement machine learning models for anomaly detection and fraud prevention using advanced statistical and AI techniques.
5. Data Engineering & Processing: Preprocess large datasets, design efficient pipelines for real-time and batch processing, and integrate multimodal data sources (images, text, audio, video).
6. Deployment & Scalability: Deploy models in production using cloud-based or edge computing solutions, ensuring performance, scalability, and cost-efficiency.
7. Research & Innovation: Stay updated on the latest AI research, evaluate emerging models, and propose enhancements to improve performance and robustness.
8. Collaboration & Integration: Work closely with software engineers, data scientists, and domain experts to integrate AI models into end-user applications.

Requirements

Technical Skills:

1. Strong experience in deep learning frameworks such as TensorFlow, PyTorch, or JAX.
2. Proficiency in computer vision techniques, including object detection (YOLO, Faster R-CNN), video analytics, and multimodal learning.
3. Hands-on experience with LLMs, transformers (GPT, BERT, T5, CLIP, etc.), and multimodal AI for text, image, and video synthesis.
4. Experience in fraud detection and/or anomaly detection using machine learning, pattern recognition, and risk modeling.
5. Experience in AWS SageMaker for model training and deployment preferred.
6. Familiarity with MLOps and deployment on cloud platforms (AWS and/or GCP preferred) or edge devices.

Soft Skills:

1. Strong problem-solving and analytical thinking abilities.
2. Ability to work in a fast-paced, research-driven environment and adapt to evolving challenges.
3. Excellent communication skills for presenting findings and collaborating across teams.

Preferred Qualifications:

1. Advanced Degree in Computer Science, AI, Machine Learning, or a related field.
2. 5 to 7 years of relevant experience.
3. Experience working with autonomous systems, robotics, or edge AI is a plus.