Asset & Wealth Management - Software Engineer, AI Platform and Services - Associate - Richardson

About the role

The AI Platform and Services VP at Marcus by Goldman Sachs will lead the development and implementation of AI-driven solutions to enhance operational efficiency and reduce risk in production environments. This role involves providing strategic direction, building tool-calling agents, and integrating AI solutions with existing systems.

Responsibilities

• Design and implement tool-calling agents that combine retrieval, structured reasoning, and secure action execution following MCP protocol.
• Engineer robust guardrails for safety, compliance, and least-privilege access.
• Build evaluation framework for open-source and foundational LLMs; implement retrieval pipelines, prompt synthesis, response validation, and self-correction loops tailored to production operations.
• Integrate agents with observability, incident management, and deployment systems to enable automated diagnostics, runbook execution, remediation, and post-incident summarization with full traceability.
• Partner with production engineers and application teams to translate production pain points into agentic AI roadmaps; define objective functions linked to reliability, risk reduction, and cost; and deliver auditable, business-aligned outcomes.
• Build validator models, adversarial prompts, and policy checks into the stack; enforce deterministic fallbacks, circuit breakers, and rollback strategies; instrument continuous evaluations for usefulness, correctness, and risk.
• Optimize cost and latency via prompt engineering, context management, caching, model routing, and distillation; leverage batching, streaming, and parallel tool-calls to meet stringent Service Level Objectives (SLOs) under real-world load.
• Build a Retrieval-Augmented Generation (RAG) pipeline: curate domain-knowledge; build data-quality validation framework; establish feedback loops and milestone framework to maintain knowledge freshness.
• Raise the bar: drive design reviews, experiment rigor, and high-quality engineering practices; mentor peers on agent architectures, evaluation methodologies, and safe deployment patterns.

Qualifications

• A Bachelor’s degree (Masters/ PhD preferred) in a computational field (Computer Science, Applied Mathematics, Engineering, or in a related quantitative discipline), with 5+ years of experience as an applied data scientist / machine learning engineer.
• 5+ years of software development in one or more languages (Python, C/C++, Go, Java); strong hands-on experience building and maintaining large-scale Python applications preferred.
• 5+ years designing, architecting, testing, and launching production ML systems, including model deployment/serving, evaluation and monitoring, data processing pipelines, and model fine-tuning workflows.
• Practical experience with Large Language Models (LLMs): API integration, prompt engineering, finetuning/adaptation, and building applications using RAG and tool-using agents (vector retrieval, function calling, secure tool execution).
• Solid grasp of applied statistics, core ML concepts, algorithms, and data structures to deliver efficient and reliable solutions.
• Strong analytical problem-solving, ownership, and urgency; ability to communicate complex ideas simply and collaborate effectively across global teams with a focus on measurable business impact.