The primary goal of the AI Innovation Acceleration team at Goldman Sachs is to demonstrate the transformative potential of AI within the firm, rapidly deliver impactful solutions, and then seamlessly transfer the code, knowledge, and ownership to the respective business and engineering teams.
Requirements
- 5+ years of hands-on experience in AI/ML development, with a proven track record of delivering end-to-end AI solutions in a professional setting.
- Demonstrated experience building and deploying end-to-end AI applications, particularly those leveraging LLMs and related frameworks.
- Strong proficiency in programming languages such as Python, along with relevant AI/ML frameworks (e.g., TensorFlow, PyTorch).
- Experience with cloud platforms (e.g., AWS, Azure, GCP) and MLOps practices for model deployment and management.
- Experience with prompt engineering, fine-tuning, Retrieval Augmented Generation (RAG), and agentic frameworks.
Responsibilities
- Lead the end-to-end development of AI/ML models and applications, from ideation and data exploration to rapid prototyping and initial deployment.
- Collaborate closely with business and engineering teams to deeply understand their challenges and customer needs, identify high-impact AI use cases, and translate business requirements into robust technical specifications and solution architectures.
- Architect, implement, and deliver scalable, robust, and maintainable AI solutions based on defined technical specifications and architectures, ensuring seamless integration with existing systems and workflows within the Goldman Sachs ecosystem.
- Facilitate effective knowledge transfer through comprehensive documentation, training sessions, mentorship, and pair-programming, empowering receiving teams to take ownership and continue the development of AI solutions.
- Stay abreast of the latest advancements in AI, machine learning, and relevant technologies, continuously evaluating and recommending new tools, techniques, and best practices to drive innovation.
Other
- Bachelor's or Master's degree in Computer Science, Data Science, or a related quantitative field.
- Excellent communication capabilities, with the ability to articulate complex technical concepts to both technical and non-technical stakeholders across all levels of the organization.
- Strong collaboration and interpersonal skills, with a passion for mentoring and enabling others.
- Proven ability to lead or significantly contribute to cross-functional projects.
