Infrastructure Engineering Intern — AI Platform Operations

Our company

At Teradata, we believe that people thrive when empowered with better information. Teradata Autonomous Knowledge Platform activates enterprise intelligence by unifying data, knowledge and business context to achieve tangible outcomes. With Teradata, organizations can provide agents with full context for impact when it matters. Our solution lets businesses connect and scale on premises, in the cloud, or through a hybrid approach. Teradata delivers real business value with AI.

 Our Internship Program

Our Mexico Intern program starts in September ending in March. We offer a fast-paced, flexible, fun environment where you will have the opportunity to work on meaningful projects and face new challenges. Our culture isn’t just about one kind of person. So many individuals make up who we are, making us that much more unique. This is what sets apart the dynamic, diverse, and collaborative environment that is Teradata. 

What You'll Do

In this role, you will document, test, and improve the infrastructure and processes behind Teradata’s internal AI platform, so that the people who run it can change it safely and the employees who depend on it can actually use it.

• Map the current state of the AI platform’s infrastructure — hosts, services, dependencies, and integration points — and produce the topology and dependency documentation the engineering team uses to plan changes.
• Write and test runbooks for recurring platform operations such as deployments, node maintenance, and connector failure and recovery, proving each one by having someone who has never performed the task follow it start to finish.
• Trace how requests move through the platform to build a dependency inventory: which components call the shared AI services, what each one does when those services are unavailable, and where that behavior is currently undefined.
• Identify and write up process gaps you run into — undocumented steps, manual handoffs, approvals with no named owner, work repeated by hand — with a specific recommendation for each, and drive the small ones to done yourself.
• Establish a usage baseline for the AI tools we provide: which are being used, by which functions, and how often, so decisions about what to expand, consolidate, or retire rest on measurement rather than anecdote.
• Contribute to active AI platform projects alongside the engineers who own them — evaluating tools, standing up test environments, and scripting repetitive setup and validation steps.
• Publish and maintain the AI Enablement intranet as the single place this documentation lives, on a publishing cadence and content standard you define and then hold.
• Translate technical change into readable communication — release notes, platform updates, hackathon and Customer Zero summaries, and short guides written for a busy employee rather than for an engineer.

What You’ll Gain

• Hands-on exposure to enterprise infrastructure at real scale — Kubernetes-based platforms, virtualization, load balancing, and the operational practices that keep production systems running.
• Ownership of a named deliverable: the platform documentation set, runbook library, and process-gap register are yours, they carry your name, and they stay in use after your internship ends.
• Regular one-to-one mentorship from the Head of AI Enablement & Strategy, plus direct working relationships with the engineers who own the systems you document.
• A working understanding of how enterprise AI actually gets adopted — governance, intake, tool evaluation, security review — and why technically sound tools still fail to land.
• Practical fluency with documentation-as-code and with the AI tooling in Teradata’s own stack, used the way practitioners use it rather than the way demos present it.
• Internal publication: what you write is read by employees across the company, on an intranet you help restructure.

Who You'll Work With

You will sit on the AI Enablement & Strategy team within Technology Intelligence Services, the group responsible for how Teradata evaluates, provisions, governs, and adopts AI internally — both homegrown solutions and third-party platforms. You will report to the Head of AI Enablement & Strategy, and you will have standing working time with the infrastructure and platform engineers who own the systems you document, because documentation written without them is fiction. Beyond your own team, your primary partners are the wider Technology Intelligence Services organization, the People Organization on enablement and training, and the Law Organization on the governance and intake side of new AI tooling. Expect to spend real time asking engineers to explain things and then writing down what you learned — that is the job, not a detour from it.

What Makes You a Qualified Candidate

Required

• Currently pursuing a bachelor’s degree or higher in Computer Science, Computer Systems Engineering, Information Technology, or a related field, with a university collaboration agreement (convenio de colaboración) available.
• Working knowledge of Linux at the command line — navigating a filesystem, reading logs, inspecting running services — and scripting ability in Python or Bash sufficient to automate a repetitive task end to end.
• Familiarity with Git, including branching and pull requests.
• Professional-level technical writing in English: able to produce documentation that a global audience can follow without it being rewritten first.
• Available for the full 12–16 week program.

Preferred

• Preferred: Exposure to containers or orchestration (Docker, Kubernetes), or to a public or private cloud platform.
• Preferred: Coursework or hands-on work with networking and reliability fundamentals — DNS, TLS, load balancing, health checks.
• Preferred: Regular use of an LLM-based tool (Claude, Copilot, or similar) in your own coursework or projects, and familiarity with a documentation-as-code workflow such as Markdown in Git.

What You’ll Bring

• You notice when a process is broken and you say so. The most valuable thing an intern does on this team is name the friction that everyone else has stopped seeing.
• You write to be understood rather than to be complete. You can tell the difference between documentation that exists and documentation someone can actually follow.
• You go find out. When a dependency is undocumented, you trace it, ask the engineer who owns it, and write down the answer where the next person will look for it.
• You are comfortable being the least experienced person in a technical conversation, and you ask the question anyway.
• You finish things. A runbook at eighty percent is a runbook nobody trusts, and a half-mapped dependency is worse than an unmapped one.
• You are appropriately skeptical of AI output, including from the tools you are documenting. You verify before you publish.
• You work in the open — small, frequent, reviewable contributions rather than one large reveal at the end of the program.

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