Full-time The Core Engineering - Technical AI Solutions Engineer - VP - London, Goldman Sachs

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

02Summary

• 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.

03Key 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.

04Qualifications

• 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.

05ABOUT 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.