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

Goldman Sachs • Richardson, TX

1d

About The Position

Marcus by Goldman Sachs As the online consumer banking business of Goldman Sachs, Marcus operates as a digital bank, providing high-yield savings accounts and Certificates of Deposit (CDs) directly to individual consumers. Marcus combines Goldman Sachs' 150+ years of expertise with intuitive digital experiences, focusing on value, transparency, and simplicity for its millions of customers, and is recognized as the largest pure online bank, delivering a fully digital experience without physical branches. The AI Platform and Services VP will provide thought leadership across the organization, with regards to concrete opportunities to use and leverage AI models and tools to accelerate program delivery end to end. This would include providing and influencing product, strategic direction and roadmap. In this role, you will be responsible for launching and implementing GenAI agentic solutions aimed at reducing the risk and cost of managing large-scale production environments with varying complexities. You will address various production runtime challenges by developing agentic AI solutions that can diagnose, reason, and take actions in production environments to improve productivity and address issues related to production support.

Requirements

• 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).
• Understanding of different LLMs, both commercial and open source, and their capabilities (e.g., OpenAI, Gemini, Llama, Qwen, Claude).
• 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.

Nice To Haves

• Proficiency building and operating on cloud infrastructure (ideally AWS), including containerized services (ECS/EKS), serverless (Lambda), data services (S3, DynamoDB, Redshift), orchestration (Step Functions), model serving (SageMaker), and infra-as-code (Terraform/CloudFormation).

Responsibilities

• Build agentic AI systems: Design and implement tool-calling agents that combine retrieval, structured reasoning, and secure action execution (function calling, change orchestration, policy enforcement) following MCP protocol. Engineer robust guardrails for safety, compliance, and least-privilege access.
• Productionize LLMs: 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 with runtime ecosystems: Connect agents to observability, incident management, and deployment systems to enable automated diagnostics, runbook execution, remediation, and post-incident summarization with full traceability.
• Collaborate directly with users: 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.
• Safety, reliability, and governance: 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.
• Scale and performance: Optimize cost and latency via prompt engineering, context management, caching, model routing, and distillation; leverage batching, streaming, and parallel tool-calls to meet stringent SLOs under real-world load.
• Build a RAG pipeline: Curate domain-knowledge; build data-quality validation framework; establish feedback loops and milestone framework 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.

Benefits

• excellent training programs designed to improve multiple facets of your skill portfolio
• comprehensive series of courses that you will have access to as your career progresses
• impressive catalogue of courses which span technical, business and leadership skills
• firmwide networks
• benefits, wellness and personal finance offerings and mindfulness programs

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level