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.
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.
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23. 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.