VP, AI Applications & Cloud-Native Engineering

Goldman Sachs; New York (NY); On-site; USD 240,000 - 280,000; Full time

Benefits offered by this job

Vacation policy

Retirement plan

Education assistência

Medical advocacy

On-site health centers

On-site gym

On-site child care

Parental resources

Adoption stipends

Job summary

Goldman Sachs is seeking an AI Application Engineer to join The Core Engineering in New York. You will drive cloud-native AI applications from ideation to production, defining where agents act and where human oversight is required, ensuring secure, reliable AI products integrated into real workflows.

You will partner with business and engineering teams to design scalable architectures, data models, and deployment strategies across AWS, Azure, or GCP, with a focus on rapid prototyping, pilot

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.

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.

Skills

Python

Java

Go

Cloud platforms

CI/CD

Communication

Problem solving

Education

Bachelor’s or Master’s in CS/Software Eng

Tools

AWS

Azure

GCP

Serverless

Containerization

Job description

Goldman Sachs is seeking an AI Application Engineer to join The Core Engineering in New York. You will drive cloud-native AI applications from ideation to production, defining where agents act and where human oversight is required, ensuring secure, reliable AI products integrated into real workflows.

You will partner with business and engineering teams to design scalable architectures, data models, and deployment strategies across AWS, Azure, or GCP, with a focus on rapid prototyping, pilot