Engineering Division

• Rapid Prototyping & End-to-End Development: Lead the end-to-end development of AI/ML models and applications, from ideation and data exploration to rapid prototyping and initial deployment.
• Business Partnership & Solution Architecture: 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.
• Solution Implementation & Delivery: 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.
• Knowledge Transfer & Enablement: 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.
• Technology & Innovation Leadership: 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.

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, or a related quantitative field.
• 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.This includes experience with prompt engineering, fine-tuning, Retrieval Augmented Generation (RAG), and agentic frameworks.
• Strong proficiency in programming languages such as Python, along with relevant AI/ML frameworks (e.g., TensorFlow, PyTorch).
• Proven ability to translate complex business requirements and customer needs into well-defined technical architectures and specifications, and to subsequently implement and deliver robust systems based on these designs.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and MLOps practices for model deployment and management.
• 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.