Machine Learning Engineer

hace 1 semana

Colombia cashea A tiempo completo


Descripción

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 .

Requisitos

Experiencia:
  • 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
Técnico:
  • 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)
Soft Skills:
  • Mentalidad de "internal tooling" (diseñar para otros desarrolladores)
  • Documentación clara y completa (technical writing)
  • Comunicación efectiva con Data Scientists (entender sus pain points)
  • Ownership y autonomía
Responsabilidades
  • 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
Ongoing:
  • 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)
Bueno que tenga
  • Experiencia con Feast (Feature Store)
  • Conocimiento Airflow/Composer (orchestration)
  • Experiencia con modelos de riesgo crediticio (NPL prediction, credit scoring)
  • Experiencia con modelos de growth (forecast, segmentación, churn)
  • Experiencia con modelos de fraude (anomaly detection, classification)
  • Experiencia en fintech o entornos regulados
  • Contribuciones open-source (MLflow, Feast, PyMC, etc.)
  • Experiencia con Evidently AI o similar (drift detection)
  • Conocimiento de prácticas de Model Governance
  • Experiencia liderando iniciativas de developer experience
  • Familiaridad con metodologías ágiles (Scrum/Kanban)

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