Job description

What We Do At Goldman Sachs, our Engineers don't just make things — we make things possible. The WM Data Engineering team within Asset & Wealth Management builds the cloud-native data platform that underpins Wealth Management globally — spanning Lakehouse architecture on AWS, ETL/ELT pipelines, data governance, and AI-powered tooling that accelerates how we build and operate at scale. Our AI Solutions Engineering function designs and delivers intelligent agent-based workflows and LLM-powered applications that transform how engineers and business teams work across the WM Data ecosystem. Who We Look For We are seeking a motivated AI Solutions Engineer to contribute to the design and delivery of production AI systems within a data engineering organization. You are intellectually curious, write clean tested code, and are excited about building AI applications at the intersection of large language models and real-world data infrastructure. Responsibilities • Build and maintain AI-powered data engineering tools — LLM agents for pipeline generation, schema mapping, data quality analysis, and migration — integrated with the WM data platform (S3, Databricks, Snowflake, Glue, Athena, MWAA) • Build and iterate on evaluation frameworks (LangSmith, RAGAS, PromptFoo) to measure and improve AI output quality across data engineering workloads • Write well-tested, production-quality code with comprehensive unit and integration tests for AI components • Implement responsible AI practices in every system: output guardrails, prompt injection defenses, PII handling, and audit logging — especially critical when operating on sensitive financial data • Implement and maintain backend services and APIs that expose AI-driven data tooling platform engineers and internal stakeholders • Collaborate with senior engineers, data architects, and business stakeholders to scope requirements, prototype solutions, and ship iteratively • Actively seek feedback, grow technical breadth across AI and data engineering, and contribute to team knowledge-sharing Basic Qualifications • 3+ years of software engineering experience, including hands-on work with machine learning models or AI application development • Proficiency in Java, Python, and SQL; hands-on experience with LLM APIs or agentic frameworks (OpenAI, Anthropic, LangChain, or similar) • Familiarity with agentic patterns: tool use, multi-step reasoning, and structured output generation • Understanding data engineering concepts — ETL/ELT pipelines, data warehousing, data lake architectures, or cloud data services (S3, Glue, Databricks, Snowflake, Athena) • Awareness of responsible AI concerns — prompt injection, hallucination risk, output guardrails, data leakage • Strong analytical and problem-solving skills; effective written and verbal communication Preferred Qualifications • Experience with AI evaluation frameworks (LangSmith, RAGAS, PromptFoo, or equivalent) • Familiarity with AWS AI/ML services (Bedrock, SageMaker, Lambda) • Familiarity with Model Context Protocol (MCP) or similar standards for tool integration with LLM agents • Exposure to pipeline orchestration tools (Airflow/MWAA, Step Functions) or Lakehouse patterns (Iceberg, Databricks, Snowflake) • Experience in financial services or regulated data environments 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: goldmansachs.com 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.

Requirements

Must-haves

Our AI Solutions Engineering function designs and delivers intelligent agent-based workflows and LLM-powered applications that transform how engineers and business teams work across the WM Data ecosystem.
Who We Look For
Build and iterate on evaluation frameworks (LangSmith, RAGAS, PromptFoo) to measure and improve AI output quality across data engineering workloads
Write well-tested, production-quality code with comprehensive unit and integration tests for AI components
Implement responsible AI practices in every system: output guardrails, prompt injection defenses, PII handling, and audit logging — especially critical when operating on sensitive financial data
Implement and maintain backend services and APIs that expose AI-driven data tooling platform engineers and internal stakeholders
Collaborate with senior engineers, data architects, and business stakeholders to scope requirements, prototype solutions, and ship iteratively
Actively seek feedback, grow technical breadth across AI and data engineering, and contribute to team knowledge-sharing
3+ years of software engineering experience, including hands-on work with machine learning models or AI application development
Proficiency in Java, Python, and SQL; hands-on experience with LLM APIs or agentic frameworks (OpenAI, Anthropic, LangChain, or similar)
Familiarity with agentic patterns: tool use, multi-step reasoning, and structured output generation
Understanding data engineering concepts — ETL/ELT pipelines, data warehousing, data lake architectures, or cloud data services (S3, Glue, Databricks, Snowflake, Athena)
Awareness of responsible AI concerns — prompt injection, hallucination risk, output guardrails, data leakage
Strong analytical and problem-solving skills; effective written and verbal communication

Nice-to-haves

Experience with AI evaluation frameworks (LangSmith, RAGAS, PromptFoo, or equivalent)
Familiarity with AWS AI/ML services (Bedrock, SageMaker, Lambda)
Familiarity with Model Context Protocol (MCP) or similar standards for tool integration with LLM agents
Exposure to pipeline orchestration tools (Airflow/MWAA, Step Functions) or Lakehouse patterns (Iceberg, Databricks, Snowflake)
Experience in financial services or regulated data environments

Application prep

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Keywords to mirror

awsaillmmachine learningjavapythonsqlml

Platform: goldman-sachs
Effort: medium

Benefits

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Wellness