The Core Engineering-L2-Bengaluru-Vice President-Software Engineering
Goldman Sachs14 hours ago
Bengaluru, IndiaStaff+
Responsibilities
- Lead the design, development, deployment, and operationalization of cloud-native AI applications using automated delivery and testing practices.
- Partner with business and engineering stakeholders to identify AI opportunities, translate requirements into architectures, and define scalable data models and technical specifications.
- Embed with internal teams to map workflows, evaluate agentic automation opportunities, iterate with users, run pilots, measure adoption, and drive production outcomes.
- Build production AI applications using LLM APIs, retrieval-augmented generation, embeddings, vector search, prompt and context management, structured outputs, response validation, and evaluation harnesses.
- Apply cloud, DevOps, observability, lifecycle management, and MLOps practices to create scalable, resilient, secure, cost-aware, and supportable applications.
- Document solutions, mentor receiving teams, and transition application code, integrations, data models, and operational practices to long-term engineering owners.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related quantitative field.
- 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 technical 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 services, 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.
- Strong 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 task decomposition, multi-step planning, approved tool or API calls, state management, permission and policy checks, failure handling, and auditable action trails.
- Preferred experience implementing agent observability and controls such as trace capture, prompt and tool-call logging, hallucination and policy checks, human approvals, incident handoff, and post-action summarization.
- 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.
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.