The Core Engineering, New York, Vice President, AI/ML Engineer

VP - AI/ML Engineer - Compliance Engineering

YOUR IMPACT

Are you passionate about leveraging cutting-edge AI/ML techniques, including Large Language Models, to solve complex, mission-critical problems in a dynamic environment? Do you want to contribute to safeguarding a leading global financial institution?

OUR IMPACT

We are Compliance Engineering, a global team of engineers and scientists dedicated to preventing, detecting, and mitigating regulatory and reputational risks across Goldman Sachs. We build and operate a suite of platforms and applications that protect the firm and its clients. We offer: Access to petabyte scale of structured and unstructured data to fuel your AI/ML models, including textual data suitable for LLM applications. The opportunity to work with state-of-the-art LLM models and agentic framework. A collaborative environment where you can learn from and contribute to a team of experienced engineers and scientists. The chance to make a tangible impact on the firm's ability to manage risk and maintain its reputation. Within Compliance Engineering, we are seeking an experienced AI/ML Engineer to join our Engineering team. This role will focus on solving highly complex business problems using AI/ML techniques, incorporating latest emerging trends om building out vertical AI agents to run on data at massive scale.

HOW YOU WILL FULFILL YOUR POTENTIAL

As a member of our team, you will: Design and architect scalable and reliable end-to-end AI/ML solutions specifically tailored for compliance applications, ensuring adherence to relevant regulatory requirements. This encompasses the development and implementation of GenAI-driven solutions, including agentic frameworks for automating compliance processes, RAG pipelines, and the creation and utilization of embeddings for compliance knowledge bases. Explore diverse AI/ML problems, such as model fine-tuning, prompt engineering, and experimentation with different algorithmic approaches to address novel business challenges. Develop, test, and maintain high-quality, production-ready code. Lead technical projects from inception to completion, providing guidance and mentorship to junior engineers. Collaborate effectively with compliance officers, legal counsel, and other stakeholders to understand business requirements and translate them into technical solutions. Participate in code reviews to ensure code quality, maintainability, and adherence to coding standards. Promote best practices for AI/ML development, including version control, testing, and documentation. Stay current with the latest advancements in AI/ML platforms, tools, and techniques to solve business problems.

QUALIFICATIONS

A successful candidate will possess the following attributes: A Bachelor's, Master's or PhD degree in Computer Science, Machine Learning, Mathematics, or a similar field of study. Preferably 7+ years AI/ML industry experience for Bachelor’s/Masters, 4+ years for PhD with a focus on Language Models. Strong foundation in machine learning algorithms, including deep learning architectures (e.g., transformers, RNNs, CNNs) Proficiency in Python and relevant libraries/frameworks such as TensorFlow, PyTorch, Hugging Face Transformers, scikit-learn. Demonstrated expertise in GenAI techniques, including but not limited to Retrieval-Augmented Generation (RAG), model fine-tuning, prompt engineering, AI agents, and evaluation techniques. Experience working with embedding models and vector databases. Experience with MLOps practices, including model deployment, containerization (Docker, kubernetes), CI/CD, and model monitoring. Strong verbal and written communication skills. Curiosity, ownership and willingness to work in a collaborative environment. Proven ability to mentor and guide junior engineers. Experience in some of the following is desired and can set you apart from other candidates: Experience with Agentic Frameworks (e.g., Langchain, AutoGen) and their application to real-world problems. Understanding of scalability and performance optimization techniques for real-time inference such as quantization, pruning, and knowledge distillation. Experience with model interpretability techniques. Prior experience in code reviews/ architecture design for distributed systems. Experience with data governance and data quality principles. Familiarity with financial regulations and compliance requirements. #J-18808-Ljbffr Goldman Sachs