AI/ML Engineer, Global Banking & Markets, RIA Engineering
About The Position
Goldman Sachs is seeking a senior AI engineering expert to develop production-grade AI solutions within its Java/JVM ecosystem. The role focuses on architecting scalable, type-safe, and observable AI agent workflows integrated into existing Java microservices, supporting collateral management, risk monitoring, and advisor support. The engineer will build end-to-end AI pipelines, including Retrieval-Augmented Generation (RAG) and agentic workflows, connected to enterprise APIs, legacy databases, and microservices. The work includes optimizing inference latency and token costs, establishing evaluation and governance frameworks, and collaborating across technical and security teams to define AI use cases and roadmaps. The role also involves building Java-based RAG ingestion pipelines, monitoring model performance, implementing human oversight and auditability, and modernizing batch processes into real-time, event-driven architectures.
Requirements
- Expert-level proficiency in Java 21+ and use of Virtual Threads/Project Loom for high-concurrency AI inference.
- Hands-on experience with leading AI frameworks and tools.
- Deep experience with Spring Boot 3.x, JPA, Sybase, and Maven or Gradle.
- Proficiency in SQL and integration with enterprise vector stores such as Milvus, Weaviate, or Pinecone using Java clients.
- Experience with Apache Kafka or RabbitMQ for orchestrating asynchronous AI agent tasks.
- Experience deploying on AWS using SDKs.
- At least 6 years of Java software engineering experience, including at least 3 years focused on production AI/ML integration.
- Understanding of RIA workflows, collateral eligibility, and the regulatory landscape of asset-based lending.
- A degree in Computer Science, Financial Engineering, or a related field.
Responsibilities
- Architect and implement end-to-end AI pipelines, including RAG and agentic workflows, integrated with enterprise APIs, legacy databases, and microservices.
- Optimize inference latency and token costs for large-scale deployments.
- Establish evaluation frameworks to measure model accuracy, mitigate hallucinations, and ensure compliance with enterprise security and privacy standards.
- Collaborate with tech leads, security leads, and software teams to identify high-impact AI use cases and define technical roadmaps.
- Monitor developments in generative AI, multimodal models, and autonomous agents, and recommend strategic changes as technology evolves.
- Build Java-based ETL pipelines to ingest and index unstructured custodial data into vector databases.
- Use Spring Boot Actuator and Micrometer to monitor LLM latency, token usage, and model drift.
- Implement human-in-the-loop patterns and structured logging to support explainable, compliant AI-driven lending decisions.
- Lead modernization of batch-oriented Java processes into real-time, event-driven AI architectures using AWS MSK and similar tools.
Benefits
- Competitive benefits and wellness offerings.
- May be eligible for a discretionary bonus if an active employee at fiscal year-end.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
