The Core Engineering-L2-Bengaluru-Vice President-Software Engineering
Goldman Sachs3 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Lead the design, development, deployment, and operationalization of cloud-native AI applications.
- Partner with business and engineering teams to identify high-impact opportunities and translate requirements into scalable architectures, data models, and technical specifications.
- Embed with internal teams to map workflows, define agentic automation and approval boundaries, run pilots, gather feedback, and measure adoption.
- Integrate LLM APIs, retrieval-augmented generation, embeddings, vector search, prompt and context management, structured outputs, validation, and evaluation harnesses.
- Apply cloud-native services, secure deployment patterns, observability, lifecycle management, and MLOps practices for scalable and supportable applications.
- Document solutions, mentor receiving teams, and transition code, integrations, data models, and operational practices to long-term owners.
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 forward-deployed or internal client-facing engineering, including workflow discovery, stakeholder engagement, prototyping, pilots, adoption measurement, and production handoff.
- Extensive experience with AWS, Azure, or GCP, including serverless, containers, managed services, deployment automation, monitoring, and cloud security.
- Experience designing cloud-based AI architectures involving managed AI services, LLM providers, retrieval and vector search, secure data access, API gateways, event-driven workflows, identity controls, observability, and resilience and cost 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 with multi-step planning, approved tool and API calls, state management, permission checks, failure handling, and auditable action trails.
- Preferred experience implementing agent observability and controls such as trace capture, prompt and tool-call logging, policy checks, human approvals, incident handoff, and post-action summaries.
- Preferred experience optimizing production AI systems through model routing, prompt compression, retrieval tuning, caching, batching, streaming, asynchronous execution, parallel tool calls, latency budgets, cost controls, and quality regression testing.
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.