AI Solutions Engineer – Vice President

The AI Innovation and Solutions (AIS) team operates with the speed and spirit of a startup, focused on rapidly prototyping and building production-grade, cloud-native AI applications that integrate cutting-edge AI capabilities to directly address the critical needs of our businesses. Our primary goal is to demonstrate the transformative potential of AI within the firm through accelerated application delivery, rapidly deploying impactful solutions, and then seamlessly transferring the application code,cloud integration patterns, robust data models, and operational knowledge to respective business and engineering teams.

This hands-on engineering role is pivotal in shaping the future of AI adoption at Goldman Sachs by building reliable,highly scalable, cloud-optimized AI-powered product sand fostering a culture of innovation andrapid, continuous delivery.

As an AI Application Engineer, you will be instrumental in designing, building, and deploying end-to-end, cloud-native AI applications that leverage advanced AI/Machine Learning solutions to drive tangible business value. You will thrive in a fast-paced environment, leveraging your expertise to translate complex business challenges and customer needs into actionable cloud-based application architectures, optimized data models, and technical specifications that incorporate AI capabilities, and then implement and deliver these systems with a focus onspeed, reliability, and operational excellence.

• Rapid Prototyping & Application Development:
  
  Lead the end-to-end development of applications that integrate and leverage AI/ML models, from architectural design,data schema design, data pipeline construction, and rapid prototyping to initial deployment and operationalization,utilizing cloud-native services (e.g., serverless, containerization, managed AI/ML platforms) and CI/CD pipelines for accelerated delivery.
  
  Implement robust MLOps practices to streamline model deployment, monitoring, and lifecycle management in cloud environments,including data versioning, feature store integration, and data pipeline management.
• Business Partnership & Solution Architecture:
  
  Collaborate closely with business and engineering teams to deeply understand their challenges and customer needs, identify high-impact opportunities to integrate AI capabilities into applications, and translate business requirements into robust cloud-optimized application architectures, scalable data models, and technical specifications for AI-powered solutions, considering scalability, cost-efficiency, security, anddata governance principles.
• Solution Implementation & Delivery:
  
  Architect, implement, and deliver scalable, robust, and maintainable cloud-native AI applications that consume and operationalize AI solutions based on defined technical specifications and architectures, ensuring seamless integration with existing systems and workflows within the Goldman Sachs ecosystem. Apply strong software engineering principles,data modeling best practices (e.g., relational, No
  
  SQL, graph),Dev Ops/MLOps best practices, and cloud security standards.
  
  Drive automation of deployment, testing, and monitoring processes to ensure rapid and reliable delivery of AI applications.
• 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 and maintenance of AI-powered applications.
• Technology & Innovation Leadership:
  
  Stay abreast of the latest advancements in application development, system integration, AI/ML technologies,data management platforms, and operational best practices, continuously evaluating and recommending new tools, techniques, and architectural patterns to drive innovation in AI application delivery.

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related quantitative field.
• 9+ years of hands-on software engineering experience, with a proven track record of building and deploying robust applications, and significant experience integrating AI/ML models.
• Demonstrated experience building and deploying end-to-end applications that leverage LLMs and related frameworks. This includes experience with prompt engineering, API integration, and working with agentic frameworks.
• Strong proficiency in programming languages such as Python, Java, or Go, along with experience integrating with relevant AI/ML frameworks (e.g.,…