Compliance-Associate-Software 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 vast amounts of structured and unstructured data to fuel your AI/ML models, including textual data suitable for LLM applications.
• The opportunity to work with the latest AI/ML technologies, including Large Language Models and cloud computing platforms.
• 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 developing, deploying, and maintaining AI/ML models, with a particular emphasis on leveraging Large Language Models.

HOW YOU WILL FULFILL YOUR POTENTIAL

As a member of our team, you will:

• Develop and deploy AI/ML models, including those based on Large Language Models (LLMs), using large-scale structured and unstructured data, addressing complex and impactful business challenges.
• Design and build scalable infrastructure for machine learning, including feature engineering pipelines and model deployment frameworks optimized for LLMs.
• Develop, productionize, and maintain AI/ML models, ensuring their accuracy, reliability, and performance, with a focus on LLM-based solutions.
• Design and execute AI/ML experiments, iteratively tuning features, prompts, and modeling approaches to optimize model performance, and meticulously documenting findings and results, especially in the context of LLMs.
• Collaborate with ML researchers to accelerate the adoption of cutting-edge AI/ML techniques and models, with a focus on advancements in Large Language Models.
• Participate in code reviews and contribute to maintaining high code quality standards.
• Contribute to the design and implementation of AI/ML model monitoring and alerting systems.
• Work with stakeholders to understand their needs and translate them into technical requirements, with an emphasis on identifying opportunities for LLM-based solutions.

QUALIFICATIONS

A successful candidate will possess the following attributes:

• A Bachelor's or Master's degree in Computer Science, or a similar field of study.
• 4+ years of hands-on experience with building scalable machine learning systems
• Solid coding skills and strong Computer Science fundamentals (algorithms, data structures, software design)
• Extensive experience with Machine Learning and Deep Learning toolkits (Tensorflow, PyTorch, Scikit-Learn, HuggingFace)
• Demonstrated experience with Large Language Models (LLMs), including model fine-tuning, prompt engineering, and evaluation techniques.
• Experience in architecting and deploying ML applications on cloud, including containerization (Docker, Kubernetes).
• Experience in working with distributed technologies like Scala, Pyspark, Iceberg, HDFS file formats (avro, parquet), AWS/ GCP, big data feature engineering.
• Experience in system design and evaluating the pros and cons of database choices, schema definition for data storage.

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.
• 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.