Corporate Planning & Management, Software Engineering, Dallas, Associate

Goldman Sachs•Dallas, TX

9h•Onsite

About The Position

Corporate Planning & Management (CPM) unifies Finance & Planning, Global Procurement, Product & Reporting and CPM Engineering teams to deliver business planning and analytics, expense management, third party risk management, sustainability strategy for our operations and supply chain, and governance strategies across the firm. CPM Engineering provides engineering solutions that enable the firm to manage third-party spend & risk management, plan budgets, forecast financial scenarios, allocate expenses and support corporate decision making in-line with the firm’s strategic objectives. This role offers direct business impact, the opportunity to build enterprise-wide solutions, cross-functional exposure, and work on complex, meaningful problems. The team provides access to modern cloud-native architectures, distributed systems, and large-scale data pipelines, and is exploring AI tools, agentic frameworks, and intelligent automation. As a Software Developer, you will design, develop, and maintain full-stack applications, build responsive user experiences and robust back-end services, leverage AI tools for development acceleration and code quality, architect and deliver multiagent AI Systems, build and maintain knowledge graph and RAG systems, establish robust governance frameworks for AI, and document agentic software development best practices. You will collaborate globally with sponsors, users, and engineering colleagues, participate in code reviews, take technical ownership of features, and stay current with advancements in AI/ML and software engineering.

Requirements

Bachelor's or master's degree in computer science, Computer Engineering, or a similar field of study.
3+ years of proficiency in Java (preferred) or another major programming language (Python), with readiness to upskill where necessary
Full-stack development experience - comfortable building both front-end (e.g., React, JavaScript/TypeScript) and back-end (e.g., Java, Spring Framework, APIs) components
Strong analytical and problem-solving skills - experience with algorithms, data structures, and software design
Excellent programming skills - developing, debugging, testing, and optimizing code
Proficiency in utilizing AI tools for software development (e.g., AI-assisted coding, code review tools, LLM-based productivity tools)
Foundational understanding of AI and agentic systems - familiarity with concepts such as large language models, prompt engineering, retrieval-augmented generation (RAG) etc.
Knowledge of cloud-native solutions in AWS
Comfortable with technical ownership, managing multiple stakeholders, and working as part of a global team

Nice To Haves

Experience with distributed databases and/or Elasticsearch
Experience with open-source messaging systems like Kafka
Systems experience in UNIX/Linux , especially in scaling for performance and debugging complex distributed systems
Experience with API design - creating interconnected services, message buses, or real-time processing systems
Exposure to GenAI techniques including RAG pipelines, model fine-tuning, agentic frameworks (e.g., LangChain, AutoGen), or embedding models and vector databases
Familiarity with MLOps practices including CI/CD for ML, model deployment, and monitoring
Knowledge of the financial industry - corporate planning, expense management, or risk functions

Responsibilities

Design, develop, and maintain full-stack applications across the entire software lifecycle from requirements gathering and architecture through implementation, testing and deployment
Build responsive, intuitive experiences and robust back-end services that power financial planning, expense management, and risk platforms
Leverage AI tools and techniques (e.g., code-generation assistants, LLM-powered automation, prompt engineering, Spec-Driven Development) to accelerate development, improve code quality, and enhance platform capabilities
Architect and deliver multiagent AI Systems using A2A protocol including orchestrator and subagent topologies
Build and maintain knowledge graph and RAG systems to enable document and data retrieval, querying and searching
Establish robust governance frameworks including logging, explainability, and auditability to ensure AI quality and reliability
Establish and document agentic software development best practices
Collaborate globally with sponsors, users, and engineering colleagues across multiple divisions to create end-to-end solutions that meet complex business requirements
Participate in code reviews to ensure quality, maintainability, and adherence to engineering best practices
Take technical ownership of features and components, managing multiple stakeholders and driving delivery within a global team
Stay current with the latest advancements in AI/ML platforms, tools, and software engineering practices to continuously improve our solutions