The Core Engineering - Technical AI Solutions Engineer - VP - London
Goldman Sachs3 days ago
London, United KingdomStaff+
Responsibilities
- Lead the design, development, deployment, and operationalization of cloud-native AI applications using automated delivery and testing practices.
- Partner with business and engineering teams to identify AI opportunities, translate requirements into architectures, and define scalable data models and technical specifications.
- Embed with internal teams to map workflows, assess agentic automation opportunities, establish control boundaries, iterate with users, and drive adoption.
- Integrate LLMs, retrieval-augmented generation, embeddings, vector search, prompt and context management, structured outputs, response validation, and evaluation harnesses into production applications.
- Apply cloud, DevOps, MLOps, observability, secure deployment, lifecycle management, cost, and resilience practices.
- Document solutions, mentor receiving teams, and support transition into long-term engineering ownership.
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 experience.
- 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, containers, managed services, deployment automation, monitoring, and cloud security standards.
- Experience designing cloud AI architectures integrating 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 monitoring.
- Excellent communication and collaboration skills with technical and non-technical stakeholders.
- Preferred experience building agentic AI systems with task decomposition, planning, tool and API calls, state management, permission checks, failure handling, and auditable action trails.
- Preferred experience implementing agent observability and controls, including trace capture, prompt and tool-call logging, hallucination and 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.
Benefits
- Goldman Sachs provides training and development opportunities, firmwide networks, wellness and personal finance offerings, and mindfulness programs.
- The employer supports reasonable accommodations for candidates with disabilities and is an equal opportunity employer.
- The role is based in London; no remote or hybrid arrangement is stated.
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.