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.