Senior Systems Engineer - Enterprise AI Platforms

About the role

Lambda, The Superintelligence Cloud, is a leader in AI cloud infrastructure serving tens of thousands of customers. Our customers range from AI researchers to enterprises and hyperscalers. Lambda's mission is to make compute as ubiquitous as electricity and give everyone the power of superintelligence. One person, one GPU.

If you'd like to build the world's best AI cloud, join us.

*Note: This position requires presence in our San Francisco of San Jose office location 4 days per week; Lambda's designated work from home day is currently Tuesday

Information Systems at Lambda is responsible for building and scaling the internal systems that power our business. We partner across the company—Finance, GTM, Engineering, and People—to implement tools, automate workflows, and ensure data flows securely and accurately. Our scope includes enterprise applications, integrations, data platform and analytics, compliance automation, and all things IT.

What You’ll Do

- Design, write, and deliver software and services to improve the availability, scalability, reliability, and efficiency of Lambda’s internal IT systems and platforms.

- Solve problems relating to mission critical services and build automation to prevent problem recurrence with the goal of automating response to all non-exceptional events.

- Work with Lambda Engineering and internal teams to Influence and create new designs, architectures, standards, and methods for large-scale distributed systems.

- Engage in service capacity planning and demand forecasting, software performance analysis, and system tuning.

- Be an excellent communicator, producing documentation and related artifacts for the systems you are responsible for.

You

- 6+ years in software engineering, site reliability engineering, or IT systems engineering where you built and ran things in code, not just configured them.

- Build & Scale Internal AI Tooling: Design and deploy low-code/no-code and custom internal platforms (using tools like Appsmith, custom Vercel-hosted frontends, etc.) integrated with LLM workflows to automate daily IT and operationalize similar work for Lambda users.

- Manage AI Infrastructure & Gateways: Deploy, monitor, and optimize internal AI proxy gateways to route model traffic, manage API rate limits, optimize latency, and control inference costs across teams.

- Automate Operational Pipelines: Integrate AI agents into internal IT workflows (Slack/Discord bots, incident triage, log parsing via Datadog, user provisioning, ticket routing).

- Maintain and Monitor Platform Health & CI/CD: Manage deployments, serverless functions, and infra pipelines (Vercel, AWS/GCP, etc) to ensure 99.9% uptime for all internal systems.

- Governance & Security: Implement guardrails, data-privacy controls, and secret management to ensure sensitive corporate data isn't exposed to third-party LLM providers.

- Have an urge to collaborate and communicate asynchronously, combined with a desire to record and document issues and solutions.