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Asset & Wealth Management-AI Solutions Engineer-Vice President-Dallas
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
Basic Qualifications
- 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
Preferred 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)
- Background in financial services or regulated data environments
We offer a wide range of health and welfare programs that vary depending on office location. These generally include medical, dental, short-term disability, long-term disability, life, accidental death, labor accident and business travel accident insurance.
We offer competitive vacation policies based on employee level and office location. We promote time off from work to recharge by providing generous vacation entitlements and a minimum of three weeks expected vacation usage each year.
We assist employees in saving and planning for retirement, offer financial support for higher education, and provide a number of benefits to help employees prepare for the unexpected. We offer live financial education and content on a variety of topics to address the spectrum of employees’ priorities.
We offer a medical advocacy service for employees and family members facing critical health situations, and counseling and referral services through the Employee Assistance Program (EAP). We provide Global Medical, Security and Travel Assistance and a Workplace Ergonomics Program. We also offer state-of-the-art on-site health centers in certain offices.
To encourage employees to live a healthy and active lifestyle, some of our offices feature on-site fitness centers. For eligible employees we typically reimburse fees paid for a fitness club membership or activity (up to a pre-approved amount).
We offer on-site child care centers that provide full-time and emergency back-up care, as well as mother and baby rooms and homework rooms. In every office, we provide advice and counseling services, expectant parent resources and transitional programs for parents returning from parental leave. Adoption, surrogacy, egg donation and egg retrieval stipends are also available.
Read more about the full suite of class-leading benefits our firm has to offer.
Opportunity Overview CORPORATE TITLE Vice President OFFICE LOCATION(S) Dallas JOB FUNCTION Software Engineering DIVISION Asset & Wealth Management
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