The Core Engineering-L2-Bengaluru-Vice President-Software Engineering
Goldman Sachs2 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Lead the design, build, 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 internal business teams to map workflows, prototype agentic automation, run pilots, measure adoption, and drive production handoff.
- Integrate LLM APIs, retrieval-augmented generation, embeddings, vector search, prompt and context management, structured outputs, validation, and evaluation capabilities.
- Apply cloud-native services, secure deployment patterns, observability, lifecycle management, and MLOps practices.
- Document solutions, mentor receiving teams, and transfer application code, integration patterns, data models, and operational practices.
- Build agentic AI systems and controls, including tool calling, permission checks, trace capture, human approvals, incident handoff, and auditable action trails when applicable.
- Optimize production AI systems through model routing, retrieval tuning, caching, batching, streaming, asynchronous execution, cost controls, and quality regression testing when applicable.
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 practices.
- Experience translating complex business requirements into cloud-optimized architectures, scalable data models, and production technical 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 systems, 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 quality, latency, and cost.
Categories
AI ApplicationsForward Deployed
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.