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Ai enablement engineer / ai platform engineer

Pelotas
Sky Systems, Inc. (SkySys)
Anunciada dia 27 abril
Descrição

Role: AI Enablement Engineer (LLM Agents | Developer Experience | AI-First Engineering)Position Type: Full-Time Contract (40hrs/week)Contract Duration: Long TermWork Schedule: 8 hours/day (Mon-Fri)Work Hours: PSTLocation: 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 doBuild 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 developmentDrive adoption of tools like Cursor, Claude Code, and CopilotCreate reusable patterns for AI-powered engineering workflowsInstrument and measure impact using DORA + SPACE metricsBuild lightweight AI infrastructure (AWS, LLM APIs, CI/CD integrations)What we’re looking for8+ years in Platform Engineering, DevOps, or Developer ExperienceStrong Python engineering backgroundHands-on experience with LLM APIs or Generative AI in productionDaily use of tools like Cursor, Claude Code, or GitHub CopilotExperience building internal developer platforms or workflowsAWS + CI/CD experience (GitLab preferred)Strong differentiators (big plus)Built AI agents or multi-agent systemsExperience with RAG, LangChain, LlamaIndex, or vector databasesExposure to AI governance, guardrails, or FinOps (token/cost management)Experience with DORA / SPACE metricsBackground in regulated industries (finance / insurance)ImportantThis 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 roleYou will help define how an entire engineering organization uses AI:Build production-grade AI agentsStandardize AI usage across teamsCreate engineering “golden paths”Directly improve developer speed and outputIf you’ve been using AI tools and thinking “this should be embedded into how teams actually build software” — this is that opportunity.

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