Full Stack AI Engineer

R
RYZ LabsSoftware Engineering
ArgentinaContractSenior
Salary not disclosed
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Job Details

Experience
5+ years
Required Skills
Node.jsPythonJavaJavascriptTypeScriptNext.jsReactNLP

Requirements

  • 5+ years of experience as a Full Stack Engineer, Platform Engineer, or AI Engineer with production system ownership.
  • Strong proficiency in JavaScript/TypeScript and a modern frontend framework (React, Next.js, or equivalent).
  • Backend development experience with Python, Java, or Node.js including secure API maintenance.
  • Hands-on experience delivering AI/ML solutions into production environments.
  • Demonstrated experience with NLP, LLMs, or GenAI (transformers, embeddings, RAG, prompt engineering).
  • Experience integrating AI solutions with ITSM tools (e.g., HALO).
  • Knowledge of REST APIs, microservices, and cloud platforms (AWS, Azure, or GCP).
  • Familiarity with MLOps, CI/CD, and model deployment/monitoring.
  • Solid understanding of ITIL/ITSM processes (Incident, Problem, Change, Request).
  • Experience working with Product Support, SRE, or NOC teams.
  • Ability to translate operational pain points into automation and AI use cases.

Responsibilities

  • Design, build, and deploy AI/ML solutions to automate ITSM ticket triage, classification, prioritization, and routing.
  • Develop NLP-based models for ticket summarization, root-cause detection, and resolution recommendation.
  • Implement AI-powered virtual agents / copilots to assist support engineers and end users.
  • Partner with Product Support, SRE, and Engineering teams to automate resolution workflows.
  • Build intelligent runbooks and self-healing automation for common incidents and service requests.
  • Enhance knowledge management by auto-generating and updating KB articles.
  • Integrate AI solutions with ITSM platforms such as HALO.
  • Develop APIs and event-driven automations across monitoring and logging tools.
  • Analyze operational data to identify and implement AI automation opportunities.
  • Train, evaluate, and monitor ML models for performance, drift, and accuracy.
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