ML Engineer

Our client is a global company in the fintech sector that is formed as a JV between two world leaders one, a global giant in the derivatives space and second, a world leader in financial research and analytics.

This company works in the area of derivatives and OTC products, which are the most complex parts of the financial market. The company is laying the foundations of a great culture and the space to build a great organization. Their current objective is to identify individuals who have high expertise, to join their existing team of experts who are spread across the world.

The Job

AI Engineer

• Develop and implement machine learning models: Design, build, and train traditional machine learning models (e.g., regression, classification, clustering) for tasks such as risk assessment, fraud detection, trade prediction, and operational efficiency.
• Explore and apply generative AI techniques: Research, experiment with, and implement generative AI models (e.g., transformers, large language models) for applications like data augmentation, synthetic data generation, anomaly detection, and natural language processing in financial contexts.
• Collaborate on AI solutions: Work closely with experienced AI engineers, data scientists, and software developers to integrate AI models into existing systems and build new AI-powered applications.
• Contribute to software development: Participate in the full software development lifecycle, including design, coding, testing, and deployment, adhering to best practices and industry standards.
• Analyze and interpret data: Analyze large datasets, identify patterns and trends, and derive insights to inform model development and business decisions.
• Stay current with AI advancements: Continuously learn and keep abreast of the latest research and trends in both traditional machine learning and generative AI.
• Communicate effectively: Clearly communicate technical concepts and findings to both technical and non-technical audiences.

Your Profile

• An Engineer with 1+ years experience in traditional machine learning and cutting-edge generative AI.
• development and implementation of AI solutions.
• Strong understanding of fundamental AI/ML concepts (e.g., supervised/unsupervised learning, deep learning, natural language processing).
• Experience with Python and relevant AI/ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Familiarity with generative AI models and frameworks (e.g., transformers, GPT).
• Proficiency in software engineering principles and practices.
• Knowledge of data structures, algorithms, and databases.