Role: AI Enablement Engineer (LLM Agents | Developer Experience | AI-First Engineering)
Position Type: Full-Time Contract (40hrs/week)
Contract Duration: Long Term
Work Schedule: 8 hours/day (Mon-Fri)
Work Hours: PST
Location: 100% Remote - (Candidates can work from anywhere in LATAM Countries)
We are building an AI-first engineering organization where AI is embedded across the entire software development lifecycle — not as an experiment, but as core infrastructure.
We're looking for a hands-on engineer to help design and build the systems, agents, and standards that make engineering teams significantly faster using AI.
This is a builder role, not a research or ML modeling role.
What you'll do
* Build AI agents for real engineering workflows (PR review, code generation, testing, debugging)
* Embed AI across the SDLC (requirements → code → CI/CD → documentation)
* Design and scale "golden paths" for AI-enabled development
* Drive adoption of tools like Cursor, Claude Code, and Copilot
* Create reusable patterns for AI-powered engineering workflows
* Instrument and measure impact using DORA + SPACE metrics
* Build lightweight AI infrastructure (AWS, LLM APIs, CI/CD integrations)
What we're looking for
* 8+ years in Platform Engineering, DevOps, or Developer Experience
* Strong Python engineering background
* Hands-on experience with LLM APIs or Generative AI in production
* Daily use of tools like Cursor, Claude Code, or GitHub Copilot
* Experience building internal developer platforms or workflows
* AWS + CI/CD experience (GitLab preferred)
Strong differentiators (big plus)
* Built AI agents or multi-agent systems
* Experience with RAG, LangChain, LlamaIndex, or vector databases
* Exposure to AI governance, guardrails, or FinOps (token/cost management)
* Experience with DORA / SPACE metrics
* Background in regulated industries (finance / insurance)
Important
This is not a Data Science or ML Research role .
We are looking for engineers who build AI systems that other engineers use every day .
Why this role
You will help define how an entire engineering organization uses AI:
* Build production-grade AI agents
* Standardize AI usage across teams
* Create engineering "golden paths"
* Directly improve developer speed and output
If you've been using AI tools and thinking "this should be embedded into how teams actually build software" — this is that opportunity.