Job Title

AI EngineerRole SummaryWe are seeking a Senior AI Engineer to lead the design and development of advanced generative, recommender, and predictive models that drive significant business value. In this role, you will ensure the performance and accuracy of solutions, staying at the forefront of the latest AI research and methodologies, particularly in natural language processing, predictive analytics, and information retrieval. You will also be responsible for promoting and implementing data science best practices throughout the organization.ResponsibilitiesAct as a thought partner to the Head of AI and Product Manager, identifying opportunities to unlock value by leveraging distinctive AI expertise in machine learning and generative AI.Lead the end-to-end process of designing, developing, validating, and deploying innovative, performant, and scalable AI engines, applying rigorous testing to ensure alignment with business objectives.Conduct advanced statistical analysis to provide actionable insights, identify trends, and measure performance.Apply an A/B testing framework to test model quality and integrate data insights into business processes.Coordinate with different functional teams to implement models, and design processes and tools to monitor and analyze model performance and data quality.Drive the AI methodology R&D agenda to ensure the company continues to offer added value by adopting best-in-class AI approaches in a fast-moving landscape.Key Skills & QualificationsDomain ExpertiseMS/PhD in Computer Science, Electronics, Computer Applications, Economics, Statistics, Mathematics, or a related technical discipline.5+ years of deep applied expertise in developing and deploying complex AI/ML solutions, preferably within a financial services context.Strong and deep knowledge of a broad set of AI methodologies, including Generative AI & Agentic AI, deep learning, natural language processing, supervised learning (classification, regression), and reinforcement learning.Proficiency in Python is required, along with multiple popular advanced analytics frameworks such as pandas, spark, scikit-learn, langchain, llamaindex, tensorflow, and pytorch.Strong applied understanding of MLOps / LLMOps best practices.Experience merging and transforming disparate internal and external data sets to create inputs for AI models.Experience with popular cloud-based ML platforms (e.g., AWS SageMaker, Azure Machine Learning) is a plus.