Asset & Wealth Management - AI Solutions Engineer - Associate - Dallas

Goldman Sachs • Dallas, TX

5h

About The Position

At Goldman Sachs, the Engineers within the WM Data Engineering team in Asset & Wealth Management are responsible for building the cloud-native data platform that supports Wealth Management globally. This platform incorporates Lakehouse architecture on AWS, ETL/ELT pipelines, data governance, and AI-powered tooling to enhance efficiency. The AI Solutions Engineering function specifically focuses on designing and delivering intelligent agent-based workflows and LLM-powered applications to transform how engineers and business teams operate within the WM Data ecosystem. The company is seeking a motivated AI Solutions Engineer to contribute to the design and delivery of production AI systems within this data engineering organization. The ideal candidate is intellectually curious, writes clean, tested code, and is enthusiastic about building AI applications that combine large language models with real-world data infrastructure.

Requirements

• 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

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

Benefits

• benefits
• wellness and personal finance offerings
• mindfulness programs
• training and development opportunities
• firmwide networks

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 5,001-10,000 employees