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About Goldman Sachs
Goldman Sachs is a leading global investment banking, securities, and investment management firm providing financial services to corporations, financial institutions, governments, and individuals.
Goldman Sachs currently has
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The Core Engineering-L2-Dallas-Vice President-Software Engineering role sits within the Shared Services AI team, functioning as an internal forward-deployed AI engineering unit. You will work directly with internal business and engineering partners to convert ambiguous operating problems into secure, reliable AI products. Responsibilities include rapid prototyping, designing cloud-optimized architectures, and productionizing cloud-native applications. You will build agentic AI solutions, RAG pipelines, and systems utilizing LLM APIs, embeddings, and vector search. Key technical requirements include proficiency in Python, Java, or Go, along with experience in AWS, Azure, or GCP, CI/CD pipelines, and MLOps. The role solves complex business problems by mapping workflows, identifying automation opportunities, and delivering scalable, cost-aware solutions that integrate managed AI services with robust identity and entitlement controls.
Lead the design, build, deployment, and operationalization of cloud-native AI applications using CI/CD pipelines and automated testing.
Partner with business teams to translate requirements into cloud-optimized architectures and scalable data models.
Embed with business units to map workflows, identify pain points, and assess agentic automation opportunities.
Build AI applications using LLM APIs, retrieval-augmented generation, vector search, and prompt management.
Apply cloud-native services and MLOps practices to ensure applications are scalable, resilient, and cost-aware.
Document solutions and mentor receiving teams to ensure a clean transition of application code and operational practices.
Define control boundaries for AI agents, including human-in-the-loop approvals and policy checks.
What we're looking for
Must have a Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related quantitative field.
Must have 9+ years of hands-on software engineering experience building, deploying, and supporting robust production applications.
Must have strong proficiency in Python, Java, or Go with experience in software engineering, testing, data modeling, and system design.
Must have experience translating complex business requirements into cloud-optimized application architectures, scalable data models, and technical specifications.
Must have experience in a forward-deployed or internal client-facing engineering model including workflow discovery, rapid prototyping, and production handoff.
Must have extensive experience with major cloud platforms such as AWS, Azure, or GCP including serverless, containerization, and cloud security.
Must have experience designing cloud-based AI architectures integrating LLM providers, RAG pipelines, vector search, and identity controls.
Must have experience integrating LLM or AI/ML capabilities into production applications including embeddings, prompt templates, and response validation.
Must have excellent communication skills to engage stakeholders, lead cross-functional delivery, and mentor receiving teams.
Preferred: Experience building agentic AI systems that decompose tasks, plan multi-step workflows, and maintain state.
Preferred: Experience implementing agent observability and controls including trace capture, hallucination checks, and human-in-the-loop approvals.
Preferred: Experience optimizing production AI systems through model routing, prompt compression, retrieval tuning, and latency budgets.