The Core Engineering- AI Solutions Engineer - VP - London
Goldman Sachs2 days ago
London, United KingdomStaff+
Responsibilities
- Lead the design, build, deployment, and operationalization of cloud-native AI applications.
- Partner with business and engineering teams to identify AI opportunities and define scalable architectures, data models, and technical specifications.
- Embed with internal teams to map workflows, assess agentic automation opportunities, run pilots, gather feedback, and drive adoption.
- Integrate LLM APIs, 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.
- Document solutions, mentor receiving teams, and transition code, integrations, data models, and operational practices to long-term owners.
- Build agentic AI systems with multi-step planning, tool and API calls, state management, permissions, failure handling, and auditable action trails.
- Implement agent observability and controls such as trace capture, 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.
- 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, prototyping, pilot execution, 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 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 with technical and non-technical stakeholders.
- Preferred experience building agentic AI systems and implementing agent observability, controls, model routing, retrieval tuning, caching, batching, streaming, asynchronous execution, and quality regression testing.
Benefits
- Goldman Sachs provides training and development opportunities, firmwide networks, wellness and personal finance offerings, and mindfulness programs.
- Reasonable accommodations are available during the recruiting process.
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.