Senior AI Systems Engineer
At a glance
Senior AI Engineering role at LatamCent. Brazil · full-time.
Pay not stated AI Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build agent-run pipelines that take recurring engineering and product work from its trigger through to resolution
- Connect production alerts to systems that diagnose and fix problems before someone is paged
- Improve agent results with stronger context, better tools and more reliable infrastructure
- Keep services and message flows reliable as usage grows, and test new models or techniques when they prove useful
- Propose and prototype product ideas, while keeping leaders informed about progress and blockers
What you bring
- At least five years of professional software development, including a recent early-stage startup role with ownership of results
- Experience in a larger organization and the ability to reason about systems at scale
- LLM and agent systems you've delivered to real users, not just demonstrations
- Fluent spoken and written English for daily conversations with founders
Who this fits
This suits a senior engineer who enjoys shaping architecture and automation, takes initiative on product ideas, and shares progress while asking for help early. The role is hybrid in Brazil, with the São Paulo area preferred, and uses the BRT time zone. It is a contract position.
From the employer
The Opportunity
AI tools have made closing tickets cheap. This team wants the engineer who asks why the ticket exists and then builds the machinery so agents handle that whole category of work, from spotting the issue to resolving it. Within three months you should have a real workflow running on agents with nobody stepping in, plus data showing it now costs less, runs quicker or produces better results. You join a flat group of senior engineers, work side by side with the CTO and CEO, and get wide latitude to change how the company builds.
What You'll Own
Convert recurring engineering and product work into pipelines that agents carry from trigger to resolution, so the team stops doing it by hand
Connect production signals to action: when errors spike (picture a burst of 500s inside half a minute), your system diagnoses and remediates before anyone gets paged
Lift agent output by giving them richer context, sharper tools and sturdier infrastructure
Keep services and message flows dependable as traffic and customer count grow
Shape what gets built: flag what doesn’t add up, pitch ideas drawn from customers and competitors, and prototype before anyone requests it
Make your work visible to leadership while it’s happening, surfacing blockers early rather than at the finish line
Scout new models and techniques and adopt the ones that earn their place
What We're Looking For
5+ years writing software professionally, including a recent stint at an early stage startup where results were yours to own
Some time at a larger organization, so you can reason about systems at scale
LLM and agent systems you shipped to real users, beyond demos
AI writes the bulk of your code today; your energy goes into the architecture and automation around it
You generate ideas unprompted and back them with product and business reasoning
You narrate progress as you work and ask for help early
Fluent English, spoken and written, at the level of daily conversations with founders
Nice to Have
Production experience with queues, pub/sub or other event based messaging
AI driven test automation, internal developer tooling or self healing systems you built
A public trail of your work: technical writing, side projects, open source or community threads
Founding or early engineer experience at a VC backed US startup
Tools and Stack
Python, TypeScript, AWS, frontier LLMs and agent frameworks.
Details
Location: Brazil, São Paulo area preferred (hybrid office).
Time Zone: BRT
Salary: $70K to $75K/year USD
Equity: Yes
Type: Contract
About the Company
A US based, venture backed B2B SaaS startup past its Series A, around 35 people, building an AI agent that learns unfamiliar enterprise apps and data on its own and carries out real business tasks inside them. Engineering is small with no management layers, and AI already writes most of the code.
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