Asset & Wealth Management - AI Solutions Engineer - Associate - Dallas
Goldman Sachs · Dallas, TX
- On site
- Full-time
- $120,000 / year
- Dallas, TX
Job highlights
- Build AI tools using LLMs for data engineering.
- Develop production AI systems in a data platform.
- Write clean, tested code for AI components.
- Implement responsible AI practices for sensitive data.
- Collaborate with engineers and stakeholders on AI solutions.
About the role
About Us
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.
Key skills/competency
- AI Solutions Engineering
- LLM Agents
- Data Engineering
- Cloud Data Services
- Python
- Java
- SQL
- Responsible AI
- Software Engineering
- AWS
Skills & topics
- AI Solutions Engineer
- Artificial Intelligence
- Machine Learning
- LLM
- Data Engineering
- Python
- Java
- SQL
- Cloud Computing
- Software Engineering
- Goldman Sachs
- Dallas
How to get hired
- Tailor your resume: Highlight relevant experience in AI, LLMs, Python, Java, SQL, and data engineering.
- Showcase your projects: Detail your contributions to building AI applications or data pipelines.
- Understand the role: Emphasize your ability to implement responsible AI and work with cloud data services.
- Prepare for technical interviews: Brush up on coding, system design, and AI/ML concepts.
- Research Goldman Sachs: Align your application with their values and commitment to innovation.
Technical preparation
Master Python, Java, and SQL for AI development.,Practice building LLM agents and API integrations.,Understand data pipelines, warehousing, and cloud services.,Study responsible AI practices and evaluation frameworks.
Behavioral questions
Describe a complex AI problem you solved.,How do you ensure code quality and testing?,Explain responsible AI to a non-technical person.,How do you collaborate with diverse stakeholders?
Frequently asked questions
- What specific AI technologies are used for the AI Solutions Engineer role at Goldman Sachs?
- The AI Solutions Engineer role at Goldman Sachs focuses on leveraging Large Language Models (LLMs) and agent-based workflows. This includes hands-on experience with LLM APIs or agentic frameworks like OpenAI, Anthropic, and LangChain, as well as building AI-powered tools for data engineering tasks such as pipeline generation and schema mapping.
- What are the key data engineering concepts relevant for the AI Solutions Engineer position at Goldman Sachs?
- For the AI Solutions Engineer position at Goldman Sachs, a strong understanding of data engineering concepts is crucial. This includes proficiency in ETL/ELT pipelines, data warehousing, data lake architectures, and cloud data services such as S3, Databricks, Snowflake, Glue, and Athena.
- How does Goldman Sachs emphasize responsible AI in this role?
- Goldman Sachs places a significant emphasis on responsible AI practices for this role. Candidates are expected to implement systems with output guardrails, prompt injection defenses, PII handling, and audit logging, particularly critical when operating on sensitive financial data.
- What programming languages are essential for the AI Solutions Engineer at Goldman Sachs?
- Proficiency in Java, Python, and SQL are essential programming languages for the AI Solutions Engineer role at Goldman Sachs. Experience with LLM APIs or agentic frameworks is also a key requirement.
- What kind of AI evaluation frameworks are relevant for this AI Solutions Engineer job?
- For this AI Solutions Engineer job, experience with AI evaluation frameworks such as LangSmith, RAGAS, and PromptFoo is preferred. Building and iterating on these frameworks to improve AI output quality across data engineering workloads is a key responsibility.
- What is the typical career progression for an AI Solutions Engineer at Goldman Sachs?
- While specific career paths vary, an AI Solutions Engineer at Goldman Sachs can expect to grow their technical breadth across AI and data engineering. Opportunities exist to collaborate with senior engineers and architects, contribute to innovative AI-driven data tooling, and potentially move into more senior engineering or lead roles within the WM Data Engineering team.