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

Goldman Sachs • Dallas, TX

2d

About The Position

At Goldman Sachs, Engineers are crucial to making things possible. The WM Data Engineering team within Asset & Wealth Management is responsible for building the cloud-native data platform that supports Wealth Management globally, encompassing Lakehouse architecture on AWS, ETL/ELT pipelines, data governance, and AI-powered tooling. 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 team is seeking a motivated AI Solutions Engineer to contribute to the design and delivery of production AI systems within a 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

• training and development opportunities
• firmwide networks
• benefits
• wellness offerings
• personal finance offerings
• mindfulness programs
• reasonable accommodations for candidates with special needs or disabilities

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