Compliance-Vice President- AI/ML Engineer

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.