
Machine Learning Engineer
12 hours ago
We are seeking a highly motivated and talented individual to join our growing team as a Machine Learning Engineer. In this role, you will have the exciting opportunity to work at the forefront of both traditional machine learning and cutting-edge generative AI within the financial services industry.
As a Machine Learning Engineer, you will contribute to the development and implementation of AI solutions that address real-world challenges. This role requires a strong foundation in AI/ML principles, software engineering skills, and a collaborative mindset.
Key Responsibilities:
- Design and develop machine learning models: Develop and implement traditional machine learning models (e.g., regression, classification, clustering) for tasks such as risk assessment, trade prediction, and operational efficiency.
- 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.
- Stay current with AI advancements: Continuously learn and keep abreast of the latest research and trends in both traditional machine learning and generative AI.
Required Skills and Qualifications:
- Education: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
- Technical Skills:
- 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., Keras, scikit-learn).
- Familiarity with generative AI models and frameworks (e.g., transformers, GPT).
- Soft Skills:
- Excellent analytical and problem-solving skills.
- Strong communication and collaboration skills.
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