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