VP AI Innovation & Solutions Engineering

VP AI Innovation & Solutions Engineering New York, NY, United States Job Description Summary 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 products and fostering a culture of innovation and rapid, 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 on speed, reliability, and operational excellence. Key Responsibilities 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, and data 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, NoSQL, graph), DevOps/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. Qualifications 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., TensorFlow, PyTorch). Proven ability to translate complex business requirements into well‑defined, cloud‑optimized application architectures, scalable data models (e.g., relational, NoSQL, graph), and technical specifications for AI‑powered systems, and to subsequently implement and accelerate delivery of robust, production‑ready systems based on these designs. Extensive experience with major cloud platforms (e.g., AWS, Azure, GCP), including cloud‑native services (serverless, containerization, managed AI/ML platforms), and a strong command of DevOps/MLOps best practices for automated deployment, monitoring, lifecycle management, data pipeline orchestration, and cloud security standards. 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. Productionize LLMs: Build evaluation framework for open‑source and foundational LLMs; implement retrieval pipelines, prompt synthesis, response validation, and self‑correction loops tailored to production operations. Integrate with runtime ecosystems: Connect agents to observability, incident management, and deployment systems to enable automated diagnostics, runbook execution, remediation, and post‑incident summarization with full traceability. Collaborate directly with users: Partner with production engineers, and application teams to translate production pain points into agentic AI roadmaps; define objective functions linked to reliability, risk reduction, and cost; and deliver auditable, business‑aligned outcomes. Scale and performance: Optimize cost and latency via prompt engineering, context management, caching, model routing, and distillation; leverage batching, streaming, and parallel tool‑calls to meet stringent SLOs under real‑world load. Build agentic AI systems: Design and implement tool‑calling agents that combine retrieval, structured reasoning, and secure action execution (function calling, change orchestration, policy enforcement) following MCP protocol. Integrate with runtime ecosystems: Connect agents to observability, incident management, and deployment systems to enable automated diagnostics, runbook execution, remediation, and post‑incident summarization with full traceability. #J-18808-Ljbffr Goldman Sachs Group, Inc.