Technology Risk - Vice President, Agentic Systems Engineer / Technical Lead, New York

YOUR IMPACT

As a Vice President within our Engineering organization, you will lead the design and development of production-grade agentic AI systems that automate complex business and cybersecurity workflows across the firm. You will help establish the architecture, controls, and engineering standards required to safely scale AI-powered automation in a highly regulated environment.

This role requires a strong software engineering foundation, deep understanding of AI systems, and the ability to partner closely with engineering, cybersecurity, and business stakeholders to transform emerging technologies into enterprise solutions that deliver measurable impact.

OUR IMPACT

Engineering is at the heart of Goldman Sachs. We build scalable platforms and technologies that power the firm's global business while maintaining the highest standards of security, reliability, and operational excellence.

Our team is focused on developing next-generation agentic systems and AI-driven platforms that enable intelligent automation, improve operational efficiency, and enhance decision-making across complex enterprise environments.

RESPONSIBILITIES

• Design and develop scalable agentic AI systems capable of reasoning, planning, retrieving information, invoking enterprise tools, and executing complex multi-step workflows.
• Build reusable platform capabilities for orchestration, memory, tool integration, observability, governance, evaluation, and human-in-the-loop controls.
• Define architecture patterns that balance AI-driven reasoning with deterministic software systems, ensuring reliability, transparency, and auditability.
• Partner with cybersecurity, engineering, and business teams to identify automation opportunities and deliver production-ready solutions.
• Develop secure integrations with internal platforms, enterprise data sources, development tooling, and operational workflows.
• Establish testing and evaluation frameworks to measure quality, reliability, performance, and business outcomes.
• Drive engineering best practices across software development, deployment, monitoring, and operational support.
• Mentor engineers and provide technical leadership across multiple initiatives.

BASIC QUALIFICATIONS

• Bachelor's degree in Computer Science, Engineering, or a related technical discipline.
• Extensive experience building and operating large-scale distributed systems, cloud-native applications, or enterprise software platforms.
• Hands-on experience developing AI-powered applications utilizing large language models, tool integration, retrieval systems, and workflow orchestration frameworks.
• Strong proficiency in Python and modern software engineering practices, including APIs, testing, CI/CD, production monitoring, and observability.
• Experience building solutions on AWS, including services such as Bedrock, Lambda, ECS/EKS, Step Functions, S3, DynamoDB, IAM, and CloudWatch.
• Strong understanding of security architecture, authentication, authorization, data protection, and governance controls.
• Proven ability to translate complex business requirements into scalable technical solutions.
• Excellent communication and stakeholder management skills.

PREFERRED QUALIFICATIONS

• Experience building AI platforms or agentic systems in cybersecurity, financial services, or other regulated industries.
• Familiarity with frameworks such as LangGraph, LangChain, AutoGen, CrewAI, Strands Agents, or similar orchestration technologies.
• Experience supporting security operations, cloud security, vulnerability management, identity and access management, or application security functions.
• Experience evaluating and implementing enterprise AI platforms, including build-versus-buy decision frameworks.
• Demonstrated track record of leading engineering teams and driving technical strategy.

SUCCESS IN THIS ROLE

Successful candidates will deliver secure, scalable, and measurable AI-driven automation solutions while establishing reusable platform capabilities that accelerate adoption across the firm. They will influence engineering standards, mentor teams, and help shape the long-term strategy for enterprise AI and agentic systems.