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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.
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The Core Engineering-L2-New York-Vice President-Software Engineering joins the Shared Services AI team as an AI Application Engineer. This forward-deployed engineering function embeds with internal business units to identify priority workflows and prototype agentic AI solutions. You will convert ambiguous operating problems into secure, reliable AI products by designing cloud-native applications and defining agentic automation boundaries. Key responsibilities include rapid prototyping, solution architecture, and productionizing AI/ML integrations. You will utilize Python, Java, or Go alongside major cloud platforms like AWS, Azure, or GCP. Technical requirements include building RAG pipelines, embeddings, vector search, and prompt management. You will also manage CI/CD pipelines, MLOps practices, and knowledge transfer to receiving teams. This role solves complex operational problems by delivering measurable business impact through scalable, production-ready AI systems.
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 and scalable data models.
Must have experience in a forward-deployed or internal client-facing engineering model including workflow discovery and rapid prototyping.
Must have extensive experience with major cloud platforms like 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 event-driven workflows.
Must have experience integrating LLM or AI/ML capabilities into production applications including embeddings, prompt management, and performance monitoring.
Must have excellent communication skills to engage stakeholders, lead cross-functional delivery, and provide documentation and mentoring.
Preferred: Experience building agentic AI systems that decompose tasks, plan multi-step workflows, and maintain state.
Preferred: Experience implementing agent observability, including trace capture, hallucination checks, and human-in-the-loop approvals.
Preferred: Experience optimizing production AI systems through model routing, prompt compression, caching, and latency budget management.