Technical AI Solutions Engineer, VP - Global Banking & Markets, RIA Engineering

Goldman Sachs · New York City · $130,000–$260,000 (stated in the posting)

About the Team

Goldman Sachs Custody & Finance Solutions provides custody and securities-based lending solutions for registered investment advisers (RIAs) to meet the complex needs of today’s sophisticated clients. Designed for independent advisors, its digital-forward platform and client-first approach provide an elevated advisor experience that supports the growth of RIAs.

The Custody & Finance Engineering team rapidly prototypes and delivers production-grade AI applications that address business and customer needs. The team combines cloud-native engineering with AI capabilities to build reliable, scalable products to support business growth and requirements.

Role Overview

As Vice President, AI Application Engineering, you will be the accountable technical leader for the applications, combining hands-on engineering with ownership of architecture, delivery, and stakeholder outcomes through AI tools and AI solutions. You will partner with business and technology leaders to translate complex challenges into cloud-native architectures and technical specifications, make and communicate key design decisions and trade-offs, and set the engineering standards the team builds against.

You will own solutions end to end, from discovery and prototyping through production deployment and ongoing operation. Every release will meet defined production-readiness criteria, including operational documentation, clear acceptance criteria, and defined support responsibilities. Working across engineering, risk, and business teams, you will drive alignment, communicate progress and risks to senior stakeholders, and mentor engineers, with a consistent focus on reliability, security, and operational excellence.

Responsibilities

Lead Discovery and Rapid Prototyping: Run discovery with stakeholders and rapidly prototype applications that integrate AI/ML models. Develop initial architectures, data schemas, and data pipelines using cloud-native services, such as serverless, containers, and managed AI/ML platforms, to validate solutions before production engineering.
Partner with the Business and Architect Solutions: Work with business and engineering stakeholders to understand their challenges and client needs and to identify high-impact opportunities for AI. Translate requirements into cloud-optimized architectures, scalable data models, and technical specifications that account for cost, security, and data governance.
Build and Deliver Production-Grade Applications: Build maintainable, cloud-native AI applications that integrate with existing Goldman Sachs systems and workflows. Apply strong practices in software engineering, data modelling (relational, NoSQL, graph), DevOps, and cloud security. Establish evaluation datasets and automated tests for AI output quality, retrieval effectiveness, and agent and tool behaviour. Automate deployment, regression testing, and monitoring, optimizing for reliability, latency, and cost.
Own Production Operations: Own the reliability, performance, and cost of AI applications in production. Define service levels, lead incident response and post-incident reviews, and manage the ongoing lifecycle of models, prompts, and integrations.
Design Safe and Controlled Agents: Design agents with least-privilege access, human approval for consequential actions, auditable tool execution, data-protection controls, and defined fallback and recovery mechanisms.
Ensure Governance and Risk Alignment: Partner with Model Risk, Compliance, Legal, and Technology Risk so that AI solutions meet firm governance, model validation, and regulatory requirements.
Set Standards and Mentor Engineers: Define architecture and engineering standards, build reusable patterns across projects, and mentor engineers on AI application development best practices.
Evaluate Emerging Technology: Evaluate emerging AI tools, frameworks, and architectural patterns, and recommend adoption where they improve delivery speed, quality, or cost.

Required Qualifications

Education: Bachelor's or master's degree in computer science, software engineering, or a related quantitative field, or equivalent practical experience.
Experience: 9+ years of hands-on software engineering experience, including building and deploying production applications and significant experience integrating AI/ML models.
Java and Enterprise Development: Advanced Java proficiency, with deep experience in Spring Boot, API integration, and Maven or Gradle.
AI Application Engineering: Proven experience building and deploying end-to-end LLM applications, including prompt engineering, retrieval (for example, vector stores or enterprise search), and tool-calling or agentic frameworks. This includes secure action execution, policy enforcement, and traceability. Experience evaluating and testing LLM-based systems.
Data Engineering: Strong SQL and data modeling skills, and experience integrating applications with enterprise databases.
Cloud Delivery and Operations: Extensive experience with at least one major cloud platform (AWS, Azure, or GCP) and cloud-native services. Strong command of CI/CD, automated testing, monitoring, and cloud security standards. Experience owning production systems, including defining service levels, leading incident response, and running post-incident reviews.
Leadership, Communication, and Enablement: Demonstrated ability to own architecture decisions, align stakeholders, and deliver across teams. Experience mentoring engineers. Able to explain complex technical concepts clearly to technical and non-technical audiences.

Preferred Qualifications

Cloud Platform: Hands-on experience with AWS
Modern Java concurrency: Experience with Java 21+ and virtual threads for high-throughput, I/O-bound integrations with AI services.
Specific enterprise technologies: Hands-on experience with JPA, Sybase, Amazon Aurora, or NoSQL databases, and Apache Kafka or RabbitMQ for asynchronous workflows.
Agent integration standards: Experience implementing Model Context Protocol (MCP) integrations and connecting agents to enterprise tools and services.
Operational automation: Experience integrating agents with observability, incident-management, and deployment systems to support automated diagnostics, runbook execution, remediation, and post-incident summarization with full traceability.
MLOps: Experience with automated model deployment, monitoring, and lifecycle management in cloud environments.
Financial-services domain knowledge: Understanding of registered investment adviser (RIA) workflows, collateral eligibility, and the regulatory landscape of securities-based lending.

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