The Core Engineering-L2-New York-Vice President-Software Engineering
Goldman Sachs13 hours ago
Responsibilities
- Lead the design, development, deployment, and operationalization of cloud-native AI applications.
- Partner with business and engineering teams to identify AI opportunities and translate requirements into scalable architectures, data models, and technical specifications.
- Embed with business teams to map workflows, assess agentic automation opportunities, define approval boundaries, run pilots, and drive adoption.
- Integrate LLM and AI/ML capabilities using retrieval-augmented generation, embeddings, vector search, prompt and context management, structured outputs, response validation, and evaluation harnesses.
- Apply cloud-native services, secure deployment patterns, observability, lifecycle management, and MLOps practices to deliver scalable and supportable applications.
- Document solutions, mentor receiving teams, and transition application code, integration patterns, data models, and operational practices to long-term owners.
- Design and implement agentic AI capabilities such as multi-step planning, tool and API calls, state management, permission checks, failure handling, and auditable action trails.
- Implement AI observability and controls, including trace capture, prompt and tool-call logging, policy checks, human approvals, incident handoff, and post-action summaries.
Requirements
- Bachelor’s or master’s degree in Computer Science, Software Engineering, or a related quantitative field.
- At least 9 years of hands-on software engineering experience building, deploying, and supporting production applications.
- Strong proficiency in Python, Java, or Go, with sound software engineering, testing, data modeling, and system design skills.
- Experience translating complex business requirements into cloud-optimized architectures, scalable data models, and production specifications.
- Experience in a forward-deployed or internal client-facing engineering model, including workflow discovery, stakeholder engagement, prototyping, pilots, adoption measurement, and production handoff.
- Extensive experience with AWS, Azure, or GCP, including serverless, containerization, managed services, automated deployment, monitoring, and cloud security standards.
- Experience designing cloud-based AI architectures integrating managed AI services, LLM providers, retrieval and vector search, secure data access, API gateways, event-driven workflows, identity and entitlement controls, observability, deployment automation, and cost and resilience trade-offs.
- Experience integrating LLM or AI/ML capabilities into production applications, including RAG pipelines, embeddings, vector databases or search indexes, prompt templates, context assembly, structured outputs, response validation, evaluation datasets, and model performance monitoring.
- Excellent communication and collaboration skills for engaging technical and non-technical stakeholders, leading cross-functional delivery, and enabling receiving teams.
- Preferred experience building agentic AI systems, implementing agent observability and controls, and optimizing production AI systems for latency, cost, reliability, and quality.
About Goldman Sachs
Goldman Sachs is a global financial services firm that provides investment banking, securities trading, and asset and wealth management to corporations, financial institutions, governments, and individuals. Its businesses generate revenue from advisory and underwriting fees, trading and financing, and management fees on client assets. Founded in 1869 and headquartered in New York City, it operates worldwide and is listed on the NYSE under the ticker GS.