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About the Role

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.

Responsibilities

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.
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 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.
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.
Apply cloud-native services, secure deployment patterns, observability, lifecycle management, and MLOps practices to ensure applications are scalable, resilient, cost-aware, and supportable.
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.

Skills

Python
Java
Go
AWS
Azure
GCP
Serverless
Containerization
Managed Services
Automated Deployment
Monitoring
Cloud Security
AI Services
LLM APIs
Retrieval-Augmented Generation (RAG)
Embeddings
Vector Search
Prompt Management
Context Management
Structured Outputs
Response Validation
Evaluation Harnesses
CI/CD Pipelines
Automated Testing
Cloud-Optimized Architectures
Scalable Data Models
Technical Specifications
Workflow Discovery
Stakeholder Engagement
Rapid Prototyping
Solution Shaping
Pilot Execution
Adoption Measurement
Production Handoff
API Gateways
Event-Driven Workflows
Identity and Entitlement Controls
Observability
Deployment Automation
Cost and Resilience Trade-offs
Vector Databases
Search Indexes
Prompt Templates
Context Assembly
Model Performance Monitoring
Agentic AI Systems
Task Decomposition
Workflow Planning
Tool/API Calling
State Maintenance
Permission and Policy Checks
Failure Path Handling
Auditable Action Trails
Agent Observability
Trace Capture
Prompt Logging
Tool-Call Logging
Hallucination Checks
Policy-Violation Checks
Human-in-the-Loop Approvals
Incident Handoff
Post-Action Summarization
Model Selection and Routing
Prompt Compression
Retrieval Tuning
Caching
Batching
Streaming Responses
Asynchronous Execution
Parallel Tool Calls
Latency Budgets
Cost Controls
Quality Regression Testing

Experience Level

9+ years of hands-on software engineering experience

Education Level

Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related quantitative field.

Benefits

Training and development opportunities
Firmwide networks
Wellness offerings
Personal finance offerings
Mindfulness programs

About the Company

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.
Learn more about our culture, benefits, and people at GS.com/careers.

Equal Opportunity

We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process.
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.