The Core Engineering-L2 Forward Deployment Engineering-Bengaluru-Vice President-Software Engineering
Goldman Sachs · Posted Nov 6
Investment banking, securities, asset management, and wealth management services
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About the role
The Core Engineering organization builds and operates platforms, applications, data solutions, models, and analytics supporting firm functions such as Risk, Controllers, Compliance, Corporate Treasury, and Human Capital Management. Its Shared Services AI team acts as an internal forward-deployed AI engineering function, embedding with business teams to prototype agentic AI solutions and productionize cloud-native applications. This role works directly with internal business and engineering partners to convert ambiguous operating problems into secure, reliable AI products, define appropriate human control gates, drive adoption, and transition solutions to receiving teams.
What you will do
- Rapid Prototyping & Application Delivery: Lead the design, build, deployment, and operationalization of cloud-native AI applications, using modern software engineering practices, CI/CD pipelines, and automated testing to accelerate reliable delivery.
- Business Partnership & Solution Architecture: Partner with business and engineering teams to identify high-impact AI opportunities, translate requirements into cloud-optimized architectures, and define scalable data models and technical specifications.
- Forward-Deployed AI Engineering: Embed with The Core business teams to map workflows, identify pain points, assess agentic automation opportunities, define control and approval boundaries, iterate with users, and drive adoption through pilots, feedback loops, and measurable outcomes.
- Production AI/ML Integration: Build AI applications using LLM APIs, retrieval-augmented generation, embeddings, vector search, prompt and context management, structured outputs, response validation, and evaluation harnesses required for production use.
- Cloud, DevOps & MLOps Execution: Apply cloud-native services, secure deployment patterns, observability, lifecycle management, and MLOps practices to ensure applications are scalable, resilient, cost-aware, and supportable.
- Knowledge Transfer & Enablement: Document solutions, mentor receiving teams, and support clean transition of application code, integration patterns, data models, and operational practices into long-term engineering ownership.
Skills used in this role
What the employer is looking for
- Must have: Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related quantitative field.
- Must have: 9+ years of hands-on software engineering experience building, deploying, and supporting robust production applications.
- Must have: Strong proficiency in Python, Java, or Go, with demonstrated ability to apply sound software engineering, testing, data modelling, and system design practices.
- Must have: Proven experience translating complex business requirements into cloud-optimized application architectures, scalable data models, and technical specifications for production delivery.
- Must have: Experience operating in a forward-deployed or internal client-facing engineering model, including workflow discovery, stakeholder engagement, rapid prototyping, solution shaping, pilot execution, adoption measurement, and production handoff to receiving teams.
- Must have: Extensive experience with major cloud platforms such as AWS, Azure, or GCP, including serverless, containerization, managed services, automated deployment, monitoring, and cloud security standards.
- Must have: Experience designing cloud-based AI solution architectures that integrate 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 across AWS, Azure, or GCP.
- Must have: Demonstrated 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.
- Must have: Excellent communication and collaboration skills, with the ability to engage technical and non-technical stakeholders, lead cross-functional delivery, and enable receiving teams through documentation, mentoring, and knowledge transfer.
Preferred qualifications
- Preferred: Experience building agentic AI systems that decompose tasks, plan multi-step workflows, call approved tools or APIs, maintain state, enforce permission and policy checks, handle failure paths, and produce auditable action trails.
- Preferred: Experience implementing agent observability and controls, including trace capture, prompt and tool-call logging, hallucination and policy-violation checks, human-in-the-loop approvals, incident handoff, and post-action summarization.
- Preferred: Experience optimizing production AI systems through model selection and routing, prompt compression, retrieval tuning, caching, batching, streaming responses, asynchronous execution, parallel tool calls, latency budgets, cost controls, and quality regression testing.
Benefits and support
- Compensation and benefits are detailed in the job posting
About Goldman Sachs
The Goldman Sachs Group, Inc. is a leading global financial institution that delivers a broad range of financial services across investment banking, securities, investment management, and consumer banking to a diverse client base. Established in 1869 and headquartered in New York City, the firm serves corporations, financial institutions, governments, and high-net-worth individuals worldwide.
- Industry
- Investment Banking
- Company size
- 47400+ employees
- Founded
- 1869
- Location
- New York, New York, USA
- Funding stage
- Public Company
Leadership
Chairman and Chief Executive Officer
President and Chief Operating Officer
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