Asset & Wealth Management-AI Solutions Engineer-Vice President-Dallas

Responsibilities

• Arcitect and deliver AI-powered data engineering solutions — LLM agents, RAG pipelines, and multi-agent workflows for pipeline generation, schema mapping, data quality, and migration — using tool-calling, stateful memory, and multi-agent coordination, integrated with the WM Lakehouse platform (S3, Databricks, Snowflake, Glue, Athena, MWAA)
• Define and maintain AI evaluation standards: offline benchmarks, prompt versioning, regression testing, and production observability — so the team always knows when a system is degrading
• Own the AI delivery lifecycle — CI/CD for model artifacts and prompt configurations, automated regression testing, and release management for LLM-powered services
• Enforce responsible AI practices: output guardrails, prompt injection defenses, and PII handling in LLM pipelines that operate on sensitive financial data
• Partner with data architects and platform engineers to ensure AI systems comply with data governance and regulatory standards (GDPR, CCPA, SOC2) and leverage Lakehouse infrastructure (Iceberg, Lake Formation)
• Establish and evangelize AI integration patterns (Model Context Protocol, AWS Bedrock) that enable data platform teams to expose their tools and data sources to LLM-based agents
• Mentor and develop associate and analyst engineers; provide technical direction and code review

Requirements

• 7+ years of software engineering experience, with 3+ years’ building production AI/ML systems and demonstrated experience in LLM-based or agentic architectures
• Proficiency in Java, Python, and SQL; strong hands-on experience with LLM APIs (OpenAI, Anthropic, or equivalent) and agentic frameworks (LangChain, LangGraph, or similar)
• Demonstrated experience designing agentic architectures: tool use, multi-agent orchestration, memory, and state management
• Working knowledge of cloud data platforms — S3, Glue, Snowflake, Athena, MWAA/Airflow, Lambda, Lakehouse patterns, and ETL/ELT workflows
• Experience building AI evaluation pipelines (LangSmith, RAGAS, PromptFoo, or equivalent)
• Excellent communication skills; proven ability to lead cross-functional technical initiatives

Qualifications

• Experience with standardized tool-integration patterns for LLM agents (e.g., Model Context Protocol) or equivalent approaches for exposing APIs and data sources to agentic systems
• Experience with data governance tooling — metadata management, data lineage, data quality frameworks, or AWS Lake Formation
• Familiarity with modern data formats and engines (Apache Iceberg, Spark, Databricks, Snowflake)
• Experience with event-driven architecture, streaming pipelines, or real-time inference serving
• Experience with infrastructure as code (AWS CDK, Terraform, or CloudFormation)

Benefits

Goldman Sachs offers a comprehensive benefits package including health insurance, retirement plans, and paid time off.

Pay

The salary range for this position is $150,000 - $200,000 annually, commensurate with experience.

Schedule

This is a full-time position.