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Cashea logo
Cashea

Compra ahora y paga después, en cuotas sin interés. El impulso que mereces.

Machine Learning Ops Lead

Machine Learning EngineerMachine Learning EngineerFull TimeRemoteSeniorTeam 501-1,000Since 2022H1B No SponsorCompany SiteLinkedIn

Location

Argentina

Posted

73 days ago

Salary

0

Seniority

Senior

Bachelor Degree5 yrs expSpanishAWSAzureDockerGCPJenkinsKubernetesPython

Job Description

Machine Learning Ops Lead

Cashea

• Construir la infraestructura de Machine Learning y MLops que permite a Data Scientists desarrollar, desplegar y mantener modelos de Machine Learning en producción siguiendo buenas prácticas industriales. • Responsable de crear herramientas, SDKs y pipelines automatizados que garantizan reproducibilidad, observabilidad y gobernanza de modelos, con enfoque en self-service y autonomía de equipos de ML. • Incluye productización y mejora constante de modelos complejos (bayesianos, ensembles) con estándares de calidad enterprise. • Construir CLI tools para deployment automatizado (ml-deploy, ml-rollback). • Implementar workflows automatizados MLflow (promotion dev → staging → prod). • Crear templates repositories con best practices (model template, API template). • Establecer buenas prácticas para productización de modelos bayesianos (sampling, inference, monitoring). • Documentar APIs y SDKs (Sphinx/MkDocs, tutoriales, examples). • Training sessions Data Scientists (certificación uso plataforma). • Desarrollar herramientas self-service para Data Scientists (80%+ autonomy target). • Implementar automated testing suites (model validation, performance checks). • Optimizar performance modelos bayesianos en producción (MCMC efficiency, inference speed). • Optimizar MLflow performance (caching, query optimization). • Construir monitoring dashboards para model health (accuracy, latency, convergence diagnostics). • Governance automation (approval gates, compliance checks). • Incident response & troubleshooting support. • Mantener y evolucionar SDKs/tooling basado en feedback DS. • Mejorar arquitectura serving modelos complejos (bayesianos, ensembles). • Documentación continua (runbooks, troubleshooting guides). • Office hours semanales Data Scientists (support + knowledge sharing).

Job Requirements

  • 5+ años de experiencia en Ciencia de Datos, Machine Learning o MLOps
  • 2+ años trabajando con MLOps tools en producción (registry, tracking, model serving, Monitoring, Versioning)
  • Experiencia construyendo SDKs/libraries Python para consumo interno
  • Experiencia con CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins)
  • Capacidad de entender modelos bayesianos, ponerlos en producción con buenas prácticas y mejorarlos
  • Python avanzado (packaging, testing, documentation)
  • MLflow (registry, tracking server, model serving, artifacts)
  • Modelos bayesianos (PyMC, Stan, o similar) - productización y optimization
  • Docker & Kubernetes básico
  • Git & GitHub/GitLab workflows
  • REST APIs design & development
  • Cloud platforms (GCP preferible, AWS/Azure acceptable)

Benefits

  • No trabajamos en piloto automático. Todo lo que hacemos es intencional.
  • Nos encanta elaborar ideas plenamente conscientes del impacto que pueden tener en nuestros usuarios.
  • Tu creatividad y curiosidad son el activo más Importante.
  • Tu voz importa. Escuchamos y damos espacio a las ideas y al feedback.
  • Todos pertenecen y lo que es importante para ti, también lo es para nosotros.
  • Valoramos la transparencia. La claridad nos mantiene conectados y con los pies en la tierra.
  • Nos enfocamos en el impacto real.

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