The Core Engineering – Technical AI Solutions Engineer – VP – London

Overview

Across Core Engineering, you will embed with business teams to identify AI opportunities and productionize cloud-native AI apps that deliver measurable impact. You translate ambiguous problems into secure, reliable AI products and define governance boundaries for agentic automation. You work with cross-functional partners on design, prototyping, and smooth handoff to receiving teams for long-term ownership. This is a hands-on, production-focused role at Goldman Sachs with a clear mission to scale AI across The Core.

Pay / Benefits

• benefits
• wellness programs
• mindfulness programs
• training and development opportunities
• accommodations for disabilities
• flexible work arrangements

Responsibilities

• Rapid prototyping, deployment, and operationalization of cloud-native AI applications with CI/CD and automated testing
• Partner with business and engineering teams to architect cloud-optimized solutions and scalable data models
• Forward-deployed AI engineering embedded with Core teams to map workflows, identify automation opportunities, and drive adoption
• Production AI/ML integration using LLMs, retrieval-augmented generation, embeddings, and validation frameworks
• Cloud, DevOps & MLOps execution including security, observability, and cost-conscious, scalable deployment
• Knowledge transfer, documentation, mentoring receiving teams, and smooth transition to long-term ownership

Key requirements

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or related quantitative field
• 9+ years of hands-on software engineering experience
• Strong proficiency in Python, Java, or Go
• Proven ability to translate business requirements into cloud-optimized architectures and production-ready data models
• Experience in a forward-deployed or internal client-facing engineering model with rapid prototyping and pilot delivery
• Extensive experience with AWS, Azure, or GCP including serverless, containers, managed services, monitoring, and security standards
• Experience designing AI solution architectures that integrate AI services, LLMs, retrieval and vector search, secure data access, APIs, and cost/resilience trade-offs
• Demonstrated experience integrating LLM/AI capabilities into production apps with RAG, embeddings, prompts, validation, and monitoring
• Excellent communication and collaboration skills with ability to engage stakeholders and enable receiving teams

• Excellent communication and collaboration
• Stakeholder engagement and cross-functional leadership
• Mentoring and knowledge transfer
• Python, Java, or Go
• Cloud platforms: AWS, Azure, GCP
• LLM APIs, retrieval-augmented generation, embeddings, vector search

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