AI Operations Engineer

Our mission is to democratize finance for all. An estimated $124 trillion of assets will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history. If you’re ready to be at the epicenter of this historic cultural and financial shift, keep reading.

We are building an elite team, applying frontier technologies to the world’s biggest financial problems. We’re looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn’t a place for complacency, it’s where ambitious people do the best work of their careers. We’re a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards. 

The Corporate Engineering team is building and scaling the next generation of enterprise AI infrastructure at Robinhood. Our mission is to make AI capabilities reliable, secure, and accessible across the company, enabling teams to build, deploy, and operate intelligent solutions at scale. From enterprise LLM integrations to agentic workflows and hybrid cloud infrastructure, we're creating the foundation that supports how Robinhood leverages AI internally.

As an AI Operations Engineer, you'll help support, maintain, and improve the AI platforms and infrastructure used by teams across Robinhood. You'll work hands-on with LLM integrations, automation, monitoring, and production systems, partnering with engineers and internal users to troubleshoot issues and build solutions that make our AI capabilities more reliable and easier to use. This is an opportunity to grow your expertise in AI infrastructure while contributing to a rapidly evolving area of Corporate Engineering.

At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams. 

• →Build internal tooling, automation, and self-service solutions that streamline AI operations, reduce manual intervention, and improve the developer experience.
• →Support and maintain enterprise AI platforms and infrastructure across cloud-based and on-premises environments, helping ensure reliable performance and availability.
• →Troubleshoot technical issues involving LLM integrations, APIs, model behavior, and system performance, working directly with developers and internal teams to identify and resolve problems.
• →Support enterprise LLM integrations, including Anthropic Claude and Google Gemini, helping monitor API usage, token consumption, performance, and reliability.
• →Implement and maintain monitoring, observability, and deployment workflows for AI systems, identifying issues and opportunities to improve operational efficiency.
• →Collaborate with Product, Software Engineering, Business Systems, and AI Strategy teams to gather feedback, investigate recurring issues, and improve the reliability and usability of enterprise AI solutions.

• Experience in IT Operations, Site Reliability Engineering, or AI infrastructure and support, with a track record of operating complex production systems.
• Practical experience working with AI platforms, LLM integrations, or AI-powered applications, including exposure to Anthropic Claude, Google Gemini, or similar models.
• Working knowledge of Linux administration and experience writing scripts or automation using Python, Bash, or similar languages.
• Familiarity with infrastructure management, configuration, or orchestration tools such as Terraform, Ansible, Chef, or Kubernetes.
• Experience troubleshooting production systems, investigating technical issues, and working with monitoring or observability tools.
• Strong problem-solving skills, curiosity, and an interest in learning emerging AI technologies and infrastructure.
• Ability to communicate technical issues clearly and collaborate with developers, engineers, and internal stakeholders.
• Bonus: Exposure to GPU infrastructure, AI workload orchestration, vector databases, RAG applications, agentic frameworks, or hybrid cloud environments.