What are the responsibilities and job description for the Asset & Wealth Management-AI Solutions Engineer-Vice President-Dallas position at Goldman Sachs?
Job Description
Job Title: AI Solutions Engineer – Vice President
Division: Asset & Wealth Management – WM Data Engineering
What We Do
At Goldman Sachs, our Engineers don't just make things — we make things possible. The Wealth Management (WM) Data Engineering team within Asset & Wealth Management builds and operates the data ecosystem that powers Wealth Management at scale — migrating legacy on-premises workloads to cloud-native platforms, delivering Lakehouse architecture on AWS, and embedding AI into how we build, operate, and evolve that infrastructure.
Our AI Solutions Engineering function bridges applied AI with data engineering — designing intelligent agent-based systems, LLM-powered tooling, and AI-augmented workflows that operate directly on the firm's data assets, pipelines, and governance surfaces.
Who We Look For
We are seeking an experienced AI Solutions Engineer to lead the design, development, and operationalization of production AI systems purpose-built for a modern data engineering organization. You are equally comfortable prototyping with large language models and reasoning about pipeline architecture, schema evolution, and query performance. You bring technical leadership that elevates the people around you.
Responsibilities
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: https://www.goldmansachs.com/careers/footer/disability-statement.html
© 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.
Job Title: AI Solutions Engineer – Vice President
Division: Asset & Wealth Management – WM Data Engineering
What We Do
At Goldman Sachs, our Engineers don't just make things — we make things possible. The Wealth Management (WM) Data Engineering team within Asset & Wealth Management builds and operates the data ecosystem that powers Wealth Management at scale — migrating legacy on-premises workloads to cloud-native platforms, delivering Lakehouse architecture on AWS, and embedding AI into how we build, operate, and evolve that infrastructure.
Our AI Solutions Engineering function bridges applied AI with data engineering — designing intelligent agent-based systems, LLM-powered tooling, and AI-augmented workflows that operate directly on the firm's data assets, pipelines, and governance surfaces.
Who We Look For
We are seeking an experienced AI Solutions Engineer to lead the design, development, and operationalization of production AI systems purpose-built for a modern data engineering organization. You are equally comfortable prototyping with large language models and reasoning about pipeline architecture, schema evolution, and query performance. You bring technical leadership that elevates the people around you.
Responsibilities
- Architect 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
- 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
- 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)
- Background in financial services or regulated data environments
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: https://www.goldmansachs.com/careers/footer/disability-statement.html
© 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.