The AI Application Engineer role within the Core Engineering Shared Services AI team focuses on developing and deploying cloud-native AI applications that deliver measurable business impact. This hands-on position involves collaborating with internal business and engineering partners to transform ambiguous problems into reliable AI solutions. The team emphasizes rapid prototyping, production AI architecture, and ensuring solutions are integrated into real workflows. The role requires a blend of technical expertise and strong communication skills to drive adoption and support long-term engineering ownership.

• Bachelor's or Master's degree in Computer Science, Software Engineering, or a related quantitative field.
• 9+ years of hands-on software engineering experience building, deploying, and supporting robust production applications.
• Strong proficiency in Python, Java, or Go, with demonstrated ability to apply sound software engineering, testing, data modeling, and system design practices.
• Proven experience translating complex business requirements into cloud-optimized application architectures, scalable data models, and technical specifications for production delivery.
• Experience operating in a forward-deployed or internal client-facing engineering model, including workflow discovery and stakeholder engagement.
• Extensive experience with major cloud platforms such as AWS, Azure, or GCP, including serverless and containerization.
• Experience designing cloud-based AI solution architectures that integrate managed AI services and secure data access.
• Demonstrated experience integrating LLM or AI/ML capabilities into production applications.
• Excellent communication and collaboration skills, with the ability to engage technical and non-technical stakeholders.

• Lead the design, build, deployment, and operationalization of cloud-native AI applications.
• Partner with business and engineering teams to identify high-impact AI opportunities and translate requirements into cloud-optimized architectures.
• Embed with The Core business teams to map workflows, identify pain points, and assess automation opportunities.
• Build AI applications using LLM APIs, retrieval-augmented generation, and other production-ready techniques.
• Apply cloud-native services and MLOps practices to ensure applications are scalable, resilient, and cost-aware.
• Document solutions, mentor receiving teams, and support the transition of application code and operational practices.

• Employees at Goldman Sachs are often offered comprehensive benefits, including medical, dental, life and disability coverage, generous vacation and holidays, and robust wellness resources such as EAP counseling, medical advocacy, on-site/virtual health services, and fitness support. Financial perks typically include retirement savings programs, live financial education, education support, and wealth-creation opportunities through equity awards and select investment programs. Many locations also provide family benefits (childcare resources, parental and family leaves, adoption/surrogacy support) and flexible work options like part-time schedules, job sharing, telecommuting, and alternate hours.