Applied AI ML Lead Engineer- (NLP/LLM/Graph)

• Research and explore new machine learning methods through independent study, attending industry-leading conferences, experimentation and participating in our knowledge sharing community
• Develop state-of-the-art machine learning models to solve real-world problems and apply them to tasks such as natural language processing, large language models or recommendation systems
• Develop state-of-the-art machine learning models to solve real-world problems and apply them to tasks such as natural language processing, speech recognition and analytics, time-series predictions or recommendation systems
• Produce outputs that lead to high-impact business applications, open-source software, patents, and publications in top AI/ML conferences and journals
• Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production
• Drive firm-wide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business

The Machine Learning Center of Excellence invites the successful candidate to apply sophisticated machine learning methods to a wide variety of complex tasks including natural language processing, large language models, and recommendation systems. The candidate must excel in working in a highly collaborative environment together with the business, technologists and control partners to deploy solutions into production. The candidate must also have a strong passion for machine learning and invest independent time towards learning, researching and experimenting with new innovations in the field. The candidate must have solid expertise in Deep Learning with hands-on implementation experience and possess strong analytical thinking, a deep desire to learn and be highly motivated., * Solid background in NLP and large language models, and a solid understanding of machine learning and deep learning methods

• Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal
• PhD in a quantitative discipline-e.g., Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science-with reasonable industry experience, or an MS with significant industry or research experience in the field
• Extensive experience with machine learning and deep learning toolkits (e.g., TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)
• Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals
• Hands-on experience building and deploying agentic AI / multi-agent systems within regulated or compliance-driven environments
• Experience with big data and scalable model training, and solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences
• Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments
• Curious, hardworking and detail-oriented, and motivated by complex analytical problems, * Strong background in mathematics and statistics and familiarity with the financial services industries and continuous integration models and unit test development
• Knowledge in search/ranking, reinforcement learning or meta-learning
• Expertise in recommendation systems
• Experience with A/B experimentation and data/metric-driven product development, cloud-native deployment in a large-scale distributed environment and ability to develop and debug production-quality code