What We Do

At Goldman Sachs, our Engineers don't just make things - we make things possible. Change the world by connecting people and capital with ideas. Solve the most challenging and pressing engineering problems for our clients. Join our engineering teams that build massively scalable software and systems, architect low latency infrastructure solutions, proactively guard against cyber threats, and leverage machine learning alongside financial engineering to continuously turn data into action. Drive new businesses, redefine finance using AI, and seize opportunities at market speed.

In an era defined by AI, Engineering isn't just central - it's the driving force behind our business. Our dynamic environment demands innovative strategic thinking and immediate, impactful solutions. Ready to push the limits of digital possibility? Begin your journey here.

Who We Look For

Goldman Sachs Engineers are at the forefront of innovation, driving solutions as creative collaborators in a fast-paced global environment. We seek individuals who evolve, adapt, and thrive on challenging problems.

We are seeking a senior engineer who can lead at the frontier of cloud-native financial systems and AI-driven engineering - someone who is energized by hard problems, brings deep technical credibility, and wants to define what the next generation of platforms looks like.

How You Will Fulfill Your Potential

• Architect the future state. Lead the design and implementation of cloud-native post trade platform. Building systems that are correct, fast, and operationally excellent.
• Own critical systems end-to-end. Take full ownership of production services from design through deployment and incident response. Being the person the team trusts to make the right call when it matters.
• Drive the cloud migration. Own key workstreams in the transformation to AWS/GCP, ensuring automated reconciliation, incremental rollout, and rigorous dual-run validation.
• Pioneer AI-driven engineering. Champion the use of AI agents and AI-enabled tooling across the team - for specification, implementation, testing, reconciliation analysis, and operational diagnostics. Help the team move from "AI-curious" to "AI-native."
• Raise the technical bar. Guide architectural decisions across the polyglot codebase, review critical code, define data dependency and orchestration patterns, and mentor engineers who want to grow into senior technical leaders.
• Partner across the firm. Work closely with business, risk, trading, and clearing teams to translate business requirements into technology-driven automation, intelligent tooling, and measurable operational improvements.
• Shape infrastructure and DevOps. Contribute to CDK infrastructure stacks, CI/CD pipelines, container image pipelines, and multi-region deployment automation - because owning a service means owning how it gets to production.

Basic Qualifications

• Proven track record as a senior software engineer with end-to-end ownership of complex, cloud-based production systems on AWS.
• Deep hands-on experience in Java building distributed calculation engines and event-driven services; comfort working across a polyglot stack.
• Experience designing and operating cloud-native platforms using microservices, gRPC/REST APIs, and managed AWS services (Fargate, Lambda, Step Functions, Aurora, ElastiCache, S3).
• Demonstrated experience using AI agents and AI-based tooling for enterprise software development - code generation, automated analysis, diagnostics, or migration validation.
• Strong testing discipline using modern frameworks and patterns, with an instinct for writing code that is correct by construction.
• Deep understanding of distributed systems - data consistency, failure modes, observability, and the operational realities of running financial infrastructure at scale.
• Exceptional communication skills and the ability to influence engineers, architects, and business stakeholders with clarity and conviction.
• Bachelor's degree in computer science, engineering, or a related field.

Preferred Experience

• AWS serverless and managed infrastructure: Fargate, Lambda, Step Functions, Aurora PostgreSQL, ElastiCache Redis, S3, EventBridge, CloudWatch.
• AI-enhanced engineering workflows: building developer or operational tooling powered by AI for diagnostics, reconciliation, code generation, or automated validation.
• Graph-based execution orchestration: DAG scheduling, topological sort, data dependency resolution with configurable wait, bypass, and fallback semantics.
• Platform migration at scale: migrating monolithic on-premises systems to cloud-native architectures with incremental rollout, dual-run reconciliation, and zero-downtime cutover.
• Infrastructure-as-code: AWS CDK (TypeScript) and DevOps practices for large-scale, multi-region platform deployments.
• Modern build systems: polyglot monorepo tooling with remote execution, container image pipelines, and protobuf code generation.
• Containerization and orchestration: Docker, Fargate task definitions, dual-mode services (one-shot compute + long-running daemon).
• Financial domain expertise: Post-trade processing, or high-volume financial risk systems.
• AI fluency: agentic AI workflows, prompt engineering and a point of view on how AI will reshape enterprise software engineering.