Engineering Software Engineer Vice President London

Summary

The AI Innovation and Solutions (AIS) team operates with the speed and spirit of a startup focused on rapidly prototyping and buildingproduction-grade cloud-native AI applicationsthat integrate cutting-edge AI capabilities to directly address the critical needs of our businesses. Our primary goal is to demonstrate the transformative potential of AI within the firm throughaccelerated application delivery rapidly deploying impactful solutions and then seamlessly transferring the application codecloud integration patterns robust data models and operational knowledge to respective business and engineering teams. This hands-on engineering role is pivotal in shaping the future of AI adoption at Goldman Sachs by building reliablehighly scalable cloud-optimized AI-powered productsand fostering a culture of innovation andrapid continuous delivery.

As an AI Application Engineer you will be instrumental in designing building and deployingend-to-end cloud-native AI applicationsthat leverage advanced AI/Machine Learning solutions to drive tangible business value. You will thrive in a fast-paced environment leveraging your expertise to translate complex business challenges and customer needs into actionablecloud-based application architectures optimized data models and technical specificationsthat incorporate AI capabilities and then implement and deliver these systems with a focus onspeed reliability and operational excellence.

Key Responsibilities

• Rapid Prototyping & Application Development:Lead the end-to-end development of applications that integrate and leverage AI/ML models from architectural designdata schema design data pipeline construction and rapid prototyping to initial deployment and operationalizationutilizing cloud-native services (e.g. serverless containerization managed AI/ML platforms) and CI/CD pipelines for accelerated robust MLOps practicesto streamline model deployment monitoring and lifecycle management in cloud environmentsincluding data versioning feature store integration and data pipeline management.
• Business Partnership & Solution Architecture:Collaborate closely with business and engineering teams to deeply understand their challenges and customer needs identify high-impact opportunities to integrate AI capabilities into applications and translate business requirements into robustcloud-optimized application architectures scalable data models and technical specificationsfor AI-powered solutions considering scalability cost-efficiency security anddata governance principles.
• Solution Implementation & Delivery:Architect implement and deliver scalable robust and maintainablecloud-native AI applicationsthat consume and operationalize AI solutions based on defined technical specifications and architectures ensuring seamless integration with existing systems and workflows within the Goldman Sachs ecosystem. Apply strong software engineering principlesdata modeling best practices (e.g. relational NoSQL graph)DevOps/MLOps best practices and cloud security automation of deployment testing and monitoring processesto ensure rapid and reliable delivery of AI applications.
• Knowledge Transfer & Enablement:Facilitate effective knowledge transfer through comprehensive documentation training sessions mentorship and pair-programming empowering receiving teams to take ownership and continue the development and maintenance of AI-powered applications.
• Technology & Innovation Leadership:Stay abreast of the latest advancements in application development system integration AI/ML technologiesdata management platforms and operational best practices continuously evaluating and recommending new tools techniques and architectural patterns to drive innovation in AI application delivery.

Qualifications

• Bachelors or Masters degree in Computer Science Software Engineering or a related quantitative field.
• 9 years of hands-on software engineering experience with a proven track record of building and deploying robust applications and significant experience integrating AI/ML models.
• Demonstrated experience building and deploying end-to-end applications that leverage LLMs and related frameworks. This includes experience with prompt engineering API integration and working with agentic frameworks.
• Strong proficiency in programming languages such as Python Java or Go along with experience integrating with relevant AI/ML frameworks (e.g. TensorFlow PyTorch).
• Proven ability to translate complex business requirements into well-defined cloud-optimized application architectures scalable data models (e.g. relational NoSQL graph) and technical specifications for AI-powered systems and to subsequently implement and accelerate delivery of robust production-ready systems based on these designs.
• Extensive experience with major cloud platforms (e.g. AWS Azure GCP) including cloud-native services (serverless containerization managed AI/ML platforms) and a strong command of DevOps/MLOps best practices for automated deployment monitoring lifecycle management data pipeline orchestration and cloud security standards.
• Excellent communication capabilities with the ability to articulate complex technical concepts to both technical and non-technical stakeholders across all levels of the organization.
• Strong collaboration and interpersonal skills with a passion for mentoring and enabling others.
• Proven ability to lead or significantly contribute to cross-functional projects.
• Productionize LLMs: Build evaluation framework for open-source and foundational LLMs; implement retrieval pipelines prompt synthesis response validation and self-correction loops tailored to production operations
• Integrate with runtime ecosystems: Connect agents to observability incident management and deployment systems to enable automated diagnostics runbook execution remediation and post-incident summarization with full traceability
• Collaborate directly with users: Partner with production engineers and application teams to translate production pain points into agentic AI roadmaps; define objective functions linked to reliability risk reduction and cost; and deliver auditable business-aligned outcomes
• Scale and performance: Optimize cost and latency via prompt engineering context management caching model routing and distillation; leverage batching streaming and parallel tool-calls to meet stringent SLOs under real-world load
• Build agentic AI systems: Design and implement tool-calling agents that combine retrieval structured reasoning and secure action execution (function calling change orchestration policy enforcement) following MCP protocol
• Integrate with runtime ecosystems: Connect agents to observability incident management and deployment systems to enable automated diagnostics runbook execution remediation and post-incident summarization with full traceability

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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