Join a dynamic, startup-like AI Innovation Acceleration team dedicated to rapidly developing and deploying cutting-edge AI solutions. This role is key to demonstrating the transformative power of AI within the firm and seamlessly integrating new capabilities into existing business and engineering processes. You'll be at the forefront of shaping AI adoption and fostering a culture of accelerated innovation.

As an AI Innovation Acceleration Specialist, you will identify, build, and implement end-to-end AI/ML solutions that deliver significant business impact. You will thrive in a fast-paced environment, translating complex business challenges and user needs into sophisticated AI architectures and technical specifications, followed by hands-on implementation and delivery.

Lead the complete lifecycle of AI/ML models and applications, from initial conception and data analysis through rapid prototyping to initial deployment.

Collaborate closely with business and engineering stakeholders to grasp their challenges and user requirements, pinpointing high-value AI use cases and converting them into detailed technical blueprints and solution designs.

Design, build, and deploy scalable, resilient, and maintainable AI solutions according to established specifications, ensuring smooth integration with current Goldman Sachs systems and workflows.

Facilitate effective knowledge transfer by providing thorough documentation, training, mentorship, and pair programming to enable receiving teams to assume ownership and drive ongoing development.

Stay current with the latest advancements in AI, machine learning, and related technologies, proactively recommending innovative tools, techniques, and best practices.

A Bachelor's or Master's degree in Computer Science, Data Science, or a related quantitative discipline is required.

Possess over 5 years of practical experience in AI/ML development, with a proven history of successfully delivering comprehensive AI solutions in professional settings.

Demonstrate expertise in building and deploying end-to-end AI applications, particularly those utilizing LLMs and associated frameworks. This includes hands-on experience with prompt engineering, fine-tuning, Retrieval Augmented Generation (RAG), and agentic frameworks.

Exhibit strong programming skills in languages like Python, coupled with proficiency in relevant AI/ML frameworks such as TensorFlow and PyTorch.

Show a clear ability to translate intricate business requirements and user needs into well-defined technical architectures and specifications, and subsequently implement and deliver robust systems based on these designs.

Experience with cloud platforms like AWS, Azure, or GCP, and familiarity with MLOps practices for model deployment and ongoing management are essential.

Possess excellent communication skills, capable of explaining complex technical concepts to diverse audiences, from technical teams to non-technical stakeholders at all organizational levels.

Demonstrate strong collaboration and interpersonal abilities, with a genuine enthusiasm for mentoring and empowering colleagues.

Show evidence of leading or making significant contributions to cross-functional projects.