Location
London, Greater London, England

Hours
Full Time

Salary
Negotiable

About the Role
The Core Engineering team builds and operates platforms, applications, data solutions, models, and analytics that power critical processes for The Core divisions of Goldman Sachs, including Risk, Controllers, Compliance, Corporate Treasury, and Human Capital Management. This centralized engineering structure promotes consistent governance and scalable solutions leveraging cloud, AI, and machine learning for innovation and efficiency. The Core Engineering’s 2,000+ engineers and strats deliver engineering, data, analytics, and quantitative capabilities across six business units: Metrics & Analytics Platforms, The Core Strats, Financials & Reporting, Non-Financial Risk & Controls, Enterprise Platforms, and Shared Services.

The Core Engineering Shared Services AI team operates as an internal forward-deployed AI engineering function, embedding with The Core business teams to identify priority workflows, prototype agentic AI solutions, and productionize cloud-native applications that deliver measurable business impact. This hands-on role combines client-facing problem discovery, rapid engineering execution, production AI architecture, and structured transition to receiving teams that will operate and extend the solutions over time.

As an AI Solutions Engineer, you will work directly with internal business and engineering partners to convert ambiguous operating problems into secure, reliable AI products. You will define where agents should act, assist, or require human approval, then design and deliver solutions adopted in real workflows and supported in production.

Key Responsibilities
- Lead design, build, deployment, and operationalization of cloud-native AI applications using modern software engineering practices, CI/CD pipelines, and automated testing.
- 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.
- Embed with 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 and feedback loops.
- Build AI applications using 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 to ensure scalable, resilient, cost-aware, and supportable applications.
- Document solutions, mentor receiving teams, and support clean transition of application code, integration patterns, data models, and operational practices into long-term engineering ownership.

Experience
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, or related quantitative field.
- 9+ years of hands-on software engineering experience building, deploying, and supporting robust production applications.
- Strong proficiency in Python, Java, or Go with sound software engineering, testing, data modelling, and system design practices.
- Proven experience translating complex business requirements into cloud-optimized application architectures, scalable data models, and technical specifications.
- Experience in a forward-deployed or internal client-facing engineering model including workflow discovery, stakeholder engagement, rapid prototyping, pilot execution, adoption measurement, and production handoff.
- Extensive experience with major cloud platforms such as AWS, Azure, or GCP, including serverless, containerization, managed services, automated deployment, monitoring, and cloud security standards.
- Experience designing cloud-based AI solution 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/resilience trade-offs.
- Demonstrated experience integrating LLM or AI/ML capabilities into production applications including RAG pipelines, embeddings, vector databases, prompt templates, context assembly, structured outputs, response validation, evaluation datasets, and model performance monitoring.
- Excellent communication and collaboration skills with ability to engage technical and non-technical stakeholders, lead cross-functional delivery, and enable receiving teams through documentation and mentoring.

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.
- 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.
- 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.