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

Summary

Key Responsibilities

• 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.

Qualifications

• 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: 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.
• ABOUT GOLDMAN SACHS
• At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world. We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers. We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html © The Goldman Sachs Group, Inc., 2023. All rights reserved.Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law.

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.