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

Job Title

AI Solutions Engineer – Vice President

Asset & Wealth Management – WM Data Engineering

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.

• 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 (Lang Chain, Lang Graph, 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 (Lang Smith, RAGAS, Prompt Foo, 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 Cloud Formation)
• Background in financial services or regulated data environments

Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veteran’s status, disability, or any other characteristic protected by applicable law. We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: