AI Enabled Modernization Agentic Software Engineer Vice President

Goldman Sachs

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Work type
On-site
Location
New York, NY
Posted
1 day ago
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Confirmed live today

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Similar $194k
$131k most similar roles pay here $247k

This listing doesn't post a salary. Most similar roles pay $151,493–$235,750.

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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 91 open roles on FindRole.

Listed pay typically runs $130,000–$250,000 across 37 roles with salary data.

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TL;DR · AI Enabled Modernization Agentic Software Engineer Vice President

AI Enabled Modernization - Agentic Software Engineer - Vice President will join the newly formed AI Agentic Systems team as a founding member to develop dynamic, goal-oriented agentic systems. The role involves designing and deploying multi-agent swarms capable of autonomous planning and execution across distributed cloud environments to modernize complex application workflows. Key responsibilities include building human-in-the-loop systems, evaluation frameworks, and observability mechanisms while integrating with APIs, CI/CD pipelines, and data platforms. Candidates must possess proficiency in Python, Go, Java, or Javascript, alongside experience with distributed systems and cloud-native architectures like AWS or Azure. Required expertise includes deploying LLM-based agents using frameworks such as LangGraph or AutoGPT. The role addresses the challenge of automating large-scale infrastructure tasks through agentic patterns in data analysis, observability, optimization, and multi-step task decomposition within a complex technology estate.

What you'll do

  • Design and deploy multi-agent systems capable of autonomous planning, reasoning, and execution across distributed cloud environments.
  • Build human-in-the-loop systems and evaluation frameworks to enable continuous improvement of AI agents.
  • Develop observability mechanisms to monitor and improve agent performance in production.
  • Integrate agentic systems with APIs, developer tools, CI/CD pipelines, and data platforms.
  • Translate ambiguous business needs into technical workflows for various firmwide business units.
  • Build automated systems for data analysis, infrastructure observability, resource optimization, or multi-step task planning.
  • Develop software to automate and validate application modernization workflows across complex technology estates.

What we're looking for

  • Proficiency in Python, Go, Java, Javascript, or similar languages.
  • Deep understanding of distributed systems and cloud-native architecture (AWS, Azure, or private clouds).
  • Experience deploying and operating LLM-based agents in production environments.
  • Familiarity with frameworks such as LangGraph, AutoGPT, or custom-built orchestration layers.
  • Experience designing benchmarks, evaluations, and quality metrics for AI systems.
  • Proven track record of applying agentic patterns to at least two areas: data analysis, observability, optimization, or planning.
  • Demonstrated interest in emerging technologies like Multi-Agent Reinforcement Learning (MARL) and swarm intelligence (preferred).
  • Familiarity with Model Context Protocol (MCP), A2A, and other agent-to-tool communication standards (preferred).

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