Vice President - Machine Learning Compliance Engineering

Role Overview: As an AI/ML Engineer in Compliance Engineering at Goldman Sachs, you will have the opportunity to leverage cutting-edge AI/ML techniques to solve complex problems and contribute to safeguarding a leading global financial institution. You will be part of a global team dedicated to preventing, detecting, and mitigating regulatory and reputational risks by building and operating platforms and applications that protect the firm and its clients. Your role will involve building and delivering high-impact AI/ML solutions for compliance applications, incorporating the latest emerging trends and building vertical AI agents to run on data at massive scale. Key Responsibilities: - Design and develop scalable and reliable end-to-end AI/ML solutions tailored for compliance applications, ensuring adherence to regulatory requirements. - Explore diverse AI/ML problems, such as model fine-tuning and experimentation with different algorithmic approaches to address novel business challenges. - Develop, test, and maintain high-quality, production-ready code. - Collaborate with stakeholders to understand business requirements and translate them into technical solutions. - Stay current with advancements in AI/ML platforms, tools, and techniques to solve business problems. Qualifications Required: - Bachelor's, Master's, or PhD degree in Computer Science, Machine Learning, Mathematics, or a related field. - Minimum 10+ years of AI/ML industry experience for Bachelors/Masters, 4+ years for PhD with a focus on Language Models. - Strong foundation in machine learning algorithms, including deep learning architectures like transformers, RNNs, CNNs. - Proficiency in Python and relevant libraries/frameworks such as TensorFlow, PyTorch, Hugging Face Transformers, scikit-learn. - Expertise in GenAI techniques, including Retrieval-Augmented Generation (RAG), model fine-tuning, prompt engineering, AI agents, and evaluation techniques. - Experience with MLOps practices, including model deployment, containerization, CI/CD, and model monitoring. - Strong verbal and written communication skills. - Curiosity, ownership, and willingness to work in a collaborative environment. - Ability to collaborate effectively with peers in a fast-paced engineering environment. Additional Company Details: Goldman Sachs is a leading global investment banking, securities, and investment management firm committed to fostering diversity and inclusion. The firm offers various opportunities for professional growth and personal development, including training, benefits, wellness programs, and more. Goldman Sachs is an equal opportunity employer that values diversity and does not discriminate based on various characteristics. Role Overview: As an AI/ML Engineer in Compliance Engineering at Goldman Sachs, you will have the opportunity to leverage cutting-edge AI/ML techniques to solve complex problems and contribute to safeguarding a leading global financial institution. You will be part of a global team dedicated to preventing, detecting, and mitigating regulatory and reputational risks by building and operating platforms and applications that protect the firm and its clients. Your role will involve building and delivering high-impact AI/ML solutions for compliance applications, incorporating the latest emerging trends and building vertical AI agents to run on data at massive scale. Key Responsibilities: - Design and develop scalable and reliable end-to-end AI/ML solutions tailored for compliance applications, ensuring adherence to regulatory requirements. - Explore diverse AI/ML problems, such as model fine-tuning and experimentation with different algorithmic approaches to address novel business challenges. - Develop, test, and maintain high-quality, production-ready code. - Collaborate with stakeholders to understand business requirements and translate them into technical solutions. - Stay current with advancements in AI/ML platforms, tools, and techniques to solve business problems. Qualifications Required: - Bachelor's, Master's, or PhD degree in Computer Science, Machine Learning, Mathematics, or a related field. - Minimum 10+ years of AI/ML industry experience for Bachelors/Masters, 4+ years for PhD with a focus on Language Models. - Strong foundation in machine learning algorithms, including deep learning architectures like transformers, RNNs, CNNs. - Proficiency in Python and relevant libraries/frameworks such as TensorFlow, PyTorch, Hugging Face Transformers, scikit-learn. - Expertise in GenAI techniques, including Retrieval-Augmented Generation (RAG), model fine-tuning, prompt engineering, AI agents, and evaluation techniques. - Experience with MLOps practices, including model deployment, containerization, CI/CD, and model monitoring. - Strong verbal and written communication skills. - Curiosity, ownership, and willingness to work in a collaborative environment. - Ability to collaborate effectively with peers in a fast-paced engineering