Lead, Machine Learning Engineer, GFT

Job Description

Job Description What is the opportunity? As a Senior Manager, AI and ML, you will be working on solving core business problems in compliance (trade surveillance, managing regulatory insights, etc) by leveraging AI and ML to optimize business processes and facilitate informed decision making. You will get the opportunity to work on cutting-edge technology and complex problems that generate significant value and have a high long-term impact on the bank’s growth. What will you do? Work on challenging business problems by leveraging large RBC data, translating them into machine learning problems and applying advanced machine learning algorithms to generate business value Lead the end-to-end lifecycle of AI/ML initiatives—from ideation and POC through development, deployment, and production support Design and implement Generative AI solutions, including Retrieval-Augmented Generation (RAG) systems, LLM-powered agents, and NLP pipelines Provide technical leadership and mentorship to team members, fostering a culture of innovation and continuous learning Collaborate with engineering, architecture, and model risk management teams to ensure solutions meet enterprise standards for scalability, security, and governance Stay current with emerging AI/ML technologies and evaluate their applicability to Compliance use cases Communicate complex technical concepts to non-technical stakeholders and senior leadership What do you need to succeed? Must have: PhD or Masters in a quantitative discipline such as computer science, mathematics, statistics, or engineering Excellent verbal and written communication skills; ability to work collaboratively with cross-functional teams including business, engineering, and risk management 7 years of expert level programming experience in Python (preferable) and other languages is a must – ability to write production level code and documentation 5 years’ experience working with large unstructured and structured datasets - exploring and understanding data, cleaning and pre-processing, performing exploratory data analysis, generating business insights and communicating them in a clear, concise manner 5 years’ experience developing and deploying models in production for real business problems Knowledge of standard machine learning libraries like numpy, pandas, scikit learn and visualization packages like seaborn or matplotllib, etc and machine learning algorithms like logistic regression, tree based models, decision trees, random forests, etc Strong understanding and work experience with core NLP and Generative AI like embeddings, text pre-processing, RNN’s, Transformer architecture, fine-tuning models, prompting techniques, etc Demonstrated experience designing and implementing Retrieval-Augmented Generation (RAG) architectures, including vector databases, embedding models, and retrieval pipelines Experience building and deploying RESTful APIs for ML model serving (FastAPI, Flask) Experience with MLOps practices, model serving, mon

Required Skills