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Software Engineer, Global Banking & Markets, Trading Technology

About the position

At Goldman Sachs, Engineers make things possible by connecting people and capital with ideas, solving challenging engineering problems for clients. They build scalable software and systems, architect low latency infrastructure, guard against cyber threats, and leverage machine learning and financial engineering to turn data into action. The role involves driving new businesses, redefining finance using AI, and seizing opportunities at market speed. In an AI-defined era, Engineering is the driving force behind the business, demanding innovative strategic thinking and impactful solutions.

Responsibilities

Designing, developing, and implementing robust, low-latency systems and innovative platforms for efficient P&L management.
Architecting and implementing production-grade AI solutions integrated into existing Java-based microservices.
Governing multiple AI agents using Goldman Sachs' AI tooling and agentic coding assistants.
Rapidly comprehending large legacy codebases, generating and assessing production-quality code, and accelerating the software development lifecycle.
Working alongside domain experts to understand production processes, challenge assumptions in a cloud-centric, AI-driven world, and define requirements for AI integration.
Orchestrating AI coding agents across all stages of the SDLC while maintaining mastery, quality, and production fitness over AI-generated work product.
Positioning the trading business to handle higher volumes at lower operational costs.
Designing and implementing high-availability, multi-region, event-driven services on a modern cloud-native platform.
Designing, building, and operating high-availability, multi-region, cloud-native services with security and comprehensive observability built in.
Developing event-driven architectures, multi-stage processing pipelines, and optimized data paths for high-throughput trade lifecycle management.
Partnering with engineers, domain experts, and global stakeholders to understand, model, and digitize business processes.
Managing the full lifecycle of software components from requirements analysis through design, development, testing, and release.
Innovating creative solutions to complex business and technical problems, building reusable capabilities.
Multiplying impact with a modern, AI-centric toolchain, orchestrating AI coding agents across all stages of the SDLC.

Requirements

2+ years of professional software development experience in Java (Java 17+ preferred).
Strong command of concurrency, collections, and modern language features in Java.
Demonstrated experience with AI-assisted engineering tools (e.g., Claude Code, GitHub Copilot Agent Mode, Devin, Gemini Code Assist).
Ability to govern AI agents, critically assess their output, and maintain quality over AI-generated work product.
Experience building event-driven and distributed systems.
Familiarity with messaging platforms (e.g., Apache Kafka), delivery guarantees, and resilience strategies.
Strong SDLC practices: version control, CI/CD pipelines, automated build/test/deploy workflows, and code quality tooling.
Solid testing discipline: unit, integration, and acceptance testing with modern frameworks.
Ability to rapidly navigate, understand, and debug large and unfamiliar codebases — with and without AI assistance.
Excellent communication and collaboration skills across technical and non-technical audiences in geographically distributed teams.

Nice-to-haves

Experience with Spring Boot.
Experience with gRPC / Protocol Buffers.
Experience with integration/orchestration frameworks (e.g., Apache Camel, Spring Integration).
Experience with pipeline/adapter design patterns (retry, dead-letter queues, error isolation).
Experience with cloud platforms (GCP, AWS).
Experience with container orchestration (Kubernetes, Docker).
Experience with JVM tuning for containerized workloads.
Experience with application instrumentation (metrics, distributed tracing, structured logging).
Experience with production support in high-availability environments.
Experience with data modeling.
Experience with SQL/NoSQL databases.
Experience with caching strategies.
Experience with performance optimization in latency-sensitive systems.
Experience with enterprise security patterns.
Experience with authentication protocols, mutual TLS, secrets management, and certificate rotation.
Equities, post-trade, or financial services experience.
Experience with trade lifecycle concepts, asset servicing, position management, reconciliation, and multi-system migration environments.
Experience with Asynchronous / non-blocking I/O frameworks (e.g., Vert.x, Netty).
Experience with multi-region / BCP architectures.
Open-source contribution experience.