VP – AI ML Engineer Compliance
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 environmentDo you want to contribute to safeguarding a leading global financial institution
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 firms 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 Bachelors, Masters 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.
