Job Description

Goldman Sachs is continuing its substantial effort to modernize its payments platform. As part of our broader strategy to digitize operations and provide financial services "as a Service," the payments platform is transforming premise-based infrastructure into a set of nimble, reliable, and scalable services designed natively for the public cloud.

Transformation starts with people. We recognize the need to uplift and modernize the skillsets of our staff while delivering value to our stakeholders. We are seeking forward-thinking engineers who bring a specific cloud engineering discipline and a mastery of AI-augmented development to change the habits and manner in which we develop software.

As a Full Stack Engineer, you will be a "force multiplier" for the team. You will not only build mission-critical payment services on AWS but also lead the adoption of agentic AI tools (such as GitHub Copilot, Claude Code, and Devin AI) to accelerate the SDLC. You will be responsible for designing, coding, and operating end-to-end solutions while mentoring others in AI-assisted engineering patterns.

Key Responsibilities

Cloud-Native Development: Design and implement scalable, secure full-stack applications using Java (Spring Boot) and Python, deployed as containerized (EKS) or server-less (Lambda) workloads on AWS.

AI-Augmented Engineering: Integrate AI tooling into daily workflows—using GitHub Copilot for real-time assistance, Claude Code for complex architectural reasoning, Devin AI for autonomous task execution and PR management and MCP.

Full Stack Ownership: Build responsive front-end components (React/TypeScript) and robust back-end APIs, ensuring seamless integration with relational (PostgreSQL) and non-relational (DynamoDB, MongoDB) databases.

Modern SDLC & SecDevOps: Manage the complete lifecycle using GitLab CI/CD, emphasizing automated testing, security scanning, and rapid, high-quality value delivery.

Modernization & Refactoring: Leverage AI agents to accelerate the migration of legacy payment logic into modern, cloud-native microservices.

Mentorship: Act as a technical authority, teaching fellow engineers how to effectively prompt, supervise, and validate AI-generated code to maintain enterprise-grade standards.

Required Skills & Experience

Baseline Engineering

Experience: 4-6 years of professional software engineering experience (Associate level).

Backend Mastery: Deep expertise in Java and the Spring Framework ecosystem. Proficiency in Python for automation and AI integration.

Frontend Proficiency: Strong experience with modern JavaScript frameworks, specifically React and TypeScript.

Data Systems: 5+ years working with SQL (relational) and experience with NoSQL databases like DynamoDB, Aurora Postgres, or Cassandra.

Messaging: String experience with MSK.

API Design: Proven track record of designing and delivering RESTful APIs and microservices architectures.

AWS & Cloud-Native Specialization

AWS Proficiency: 2+ years of hands-on experience with AWS services, including Lambda, ECS/EKS, S3, IAM, KMS, and CloudFormation/CDK.

Containerization: Strong knowledge of Docker and Kubernetes (EKS) for orchestrating distributed systems.

Security: Experience implementing a SecDevOps mindset, including identity propagation and secure secret management in the cloud.

AI-Augmentation & Productivity

AI Tooling: Demonstrated proficiency in using AI coding assistants (e.g., GitHub Copilot) and agentic AI (e.g., Claude Code, Devin AI) to improve velocity and code quality.

Prompt Engineering: Ability to design structured prompts and workflows for AI agents to handle multi-step engineering tasks like unit test generation and documentation.

Critical Supervision: Strong ability to critically evaluate AI-generated outputs for correctness, security, and performance.

Preferred Qualifications

Familiarity with financial messaging standards (e.g., ISO 20022).

Contributions to open-source AI agent frameworks or internal AI-driven automation initiative