Goldman Sachs
Website:
goldmansachs.com
Company:
https://www.linkedin.com/company/goldman-sachs
Seniority: Associate
Industries: Financial Services
Job details:
Job Description
In Internal Audit, we ensure that Goldman Sachs maintains effective controls by assessing the reliability of financial reports, monitoring the firm’s compliance with laws and regulations, and advising management on developing smart control solutions. Our group has unique insight on the financial industry and its products and operations. We’re looking for detail-oriented team players who have an interest in financial markets and want to gain insight into the firm’s operations and control processes.
Objective is to hire AI Engineer at an Associate level who is familiar with Agentic AI work flows having exposure to Internal audit domain or implemented any AI solutions in Audit space targeting operating efficiencies/automation.
About The Role
We are seeking a highly skilled
Data Analytics & Agentic AI Engineer with 4 to 6 years of professional software engineering experience to lead the design and development of autonomous, multi-agent AI systems and advanced Generative AI workflows.
In this role, you will focus on applying cutting-edge artificial intelligence to automate complex workflows, analyze enterprise data, and streamline processes within the Risk, Compliance, Internal Audit domains ensuring alignment with Internal audit AI integration targeting operating efficiencies.
You will bridge the gap between advanced AI engineering and enterprise business logic, building intelligent agents capable of reasoning, planning, and executing tasks such as automated document analysis, compliance checks, anomaly detection, and report generation.
Key Responsibilities
- Architect & Build Agentic AI Systems: Design and implement sophisticated single- and multi-agent systems using frameworks like LangGraph, CrewAI, AutoGen or Google ADK to automate end-to-end business workflows and compliance monitoring.
- Develop Advanced RAG & Reasoning Pipelines: Build and maintain robust Retrieval-Augmented Generation (RAG) pipelines to ground agent decisions in authoritative regulatory guidelines, internal corporate policies, and large document repositories.
- Automate Enterprise Workflows: Create tool-calling agents capable of executing secure actions, such as querying enterprise databases, extracting key information from unstructured documents, and generating structured compliance reports.
- Data Analysis & Anomaly Detection: Develop AI-driven pipelines to analyze large-scale transactional and operational data, identifying outliers, discrepancies, and potential risks.
- Implement AI Governance & Guardrails: Engineer robust safety, compliance, and security guardrails (e.g., using Model Context Protocol (MCP), role-based access controls, and PII masking) to ensure AI agents operate transparently, securely, and with full auditability.
- Technical Leadership & Mentorship: Provide technical guidance to cross-functional teams, define AI architecture patterns, and establish rigorous evaluation methodologies (e.g., RAGAS, TruLens) for LLM outputs.
Key Requirements
- Professional Experience: 5–6 years of hands-on software engineering experience, with at least 2–3 years dedicated to building and deploying production-grade GenAI and Agentic AI solutions.
- Domain Familiarity: Strong general understanding of risk management, compliance and Internal Audit workflows.
- Core Programming & Software Engineering: Exceptional proficiency in Python, clean code practices, object-oriented programming (OOP), design patterns, and enterprise-grade CI/CD pipelines.
- Generative AI & LLM Orchestration:
- Deep understanding of LLM concepts (prompt engineering, function calling, context window management, and model routing).
- Hands-on experience with LangChain and advanced agentic frameworks (LangGraph, CrewAI, AutoGen, or Agno).
- Experience with vector databases (e.g., Pinecone, Milvus, Weaviate, Chroma) and semantic search.
- AI Evaluation & Observability: Experience implementing LLM evaluation frameworks (e.g., RAGAS, PromptBench) and continuous monitoring systems to track agent behavior and prevent hallucinations
- Strong verbal and written communication skills.
- Curiosity, ownership and willingness to work in a collaborative environment.
- Demonstrated ability to collaborate effectively with peers and contribute in a highly technical, fast-paced engineering environment.
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.
We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers.
We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html
© The Goldman Sachs Group, Inc., 2023. All rights reserved.
Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law.
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