Software Engineering

Goldman Sachs; Greater London; Hybrid; GBP 150.000 - 190.000; Vollzeit

Zusammenfassung

Goldman Sachs in London seeks a VP-level AI Solutions Engineer within Core Engineering Shared Services to design and deliver cloud-native AI applications that drive measurable business impact.

You will partner with internal teams to map workflows, define agent boundaries, and productionize AI solutions using LLMs, vector search, and MLOps. Strong communication and cross-functional collaboration are essential.

Qualifikationen

• Bachelor’s or Master’s degree in CS or related quantitative field.
• 9+ years of hands-on software engineering experience.
• Proficiency in Python, Java, or Go with strong software engineering practices.
• Experience translating complex business requirements into cloud-optimized architectures and scalable data models.
• Experience operating in a forward-deployed or internal client-facing engineering model.
• Extensive experience with AWS, Azure, or GCP including serverless, containers, and security practices.
• Experience designing cloud-based AI solution architectures with LLMs, embeddings, vector search, and secure data access.
• Excellent communication and collaboration skills across technical and non-technical stakeholders.

Aufgaben

• Rapid Prototyping, deployment, and operationalization of cloud-native AI applications using CI/CD and automated testing.
• Partner with business and engineering teams to identify AI opportunities and define scalable data models and specs.
• Embed with Core teams to map workflows, identify automation opportunities, and drive adoption through pilots and measurable outcomes.
• Build AI applications using LLMs, retrieval-augmented generation, embeddings, vector search, and prompt/context management.
• Apply cloud-native services, observability, and MLOps practices to ensure scalable, cost-aware production solutions.

Kenntnisse

Python

Java

Go

Cloud platforms (AWS/Azure/GCP)

CI/CD & DevOps

AI/ML in production

Distributed systems

Security and data protection

Ausbildung

Bachelor’s or Master’s in Computer Science / Software Engineering / related field

Tools

LLM APIs

Vector search

Kubernetes

CI/CD pipelines

Jobbeschreibung

The Core Engineering- AI Solutions Engineer - VP - London location_on London, Greater London, England, United Kingdom

The Core Engineering builds and operates the platforms, applications, data solutions, models, and analytics that power critical processes for The Core divisions of the firm (e.g., Risk, responsible for the risk profile of firm activities; Controllers, responsible for the financial control and reporting obligations; Compliance, responsible for the firm’s compliance, regulatory, and reputational risks; Corporate Treasury, responsible for the firm’s liquidity, funding, balance sheet, etc.; and Human Capital Management, responsible for attracting, developing, and managing a global workforce). A centralized engineering structure in support of The Core enables a common platform model and operating framework that 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 within six business units:

• Metrics & Analytics Platforms: responsible for the measurement and management of the firm’s risk, capital, and liquidity for The Core functions
• The Core Strats: responsible for the development and implementation of models and other quantitative methodologies, including the accuracy and attribution of modeled metrics
• Financials & Reporting: responsible for facilitating the production of the firm’s financials and a wide range of reporting functions
• Non-Financial Risk & Controls: responsible for non-financial risk and control processes
• Enterprise Platforms: responsible for platforms and applications that support critical operational processes across The Core such as payments, people processes, and procurement
• Shared Services: responsible for driving the adoption of consistent engineering strategy, including data platforms, cloud, and AI enablement, as well as the management of technology risk

Summary

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 Application 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, where they should assist, and where human approval or control gates are required, then design and deliver solutions that are adopted in real workflows and can be supported in production.

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.

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 robust production applications.

Strong proficiency in Python, Java, or Go, with demonstrated ability to apply 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 for production delivery.

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.

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 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.

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.

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

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.

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

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.

Healthcare & Medical Insurance

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Financial Wellness & Retirement

We assist employees in saving and planning for retirement, offer financial support for higher education, and provide a number of benefits to help employees prepare for the unexpected. We offer live financial education and content on a variety of topics to address the spectrum of employees’ priorities.

Health Services

We offer a medical advocacy service for employees and family members facing critical health situations, and counseling and referral services through the Employee Assistance Program (EAP). We provide Global Medical, Security and Travel Assistance and a Workplace Ergonomics Program. We also offer state‑of‑the‑art on‑site health centers in certain offices.

Fitness

To encourage employees to live a healthy and active lifestyle, some of our offices feature on‑site fitness centers. For eligible employees we typically reimburse fees paid for a fitness club membership or activity (up to a pre‑approved amount).

Child Care & Family Care

We offer on‑site child care centers that provide full‑time and emergency back‑up care, as well as mother and baby rooms and homework rooms. In every office, we provide advice and counseling services, expectant parent resources and transitional programs for parents returning from parental leave. Adoption, surrogacy, egg donation and egg retrieval stipends are also available.

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