ML Ops Lead: Production AI & Advanced Analytics

Resilient; Comisión de Fomento de Perú; Presencial; ARS 135.601.000 - 210.935.000; A tiempo parcial

Ventajas ofrecidas por este puesto de trabajo

BYOD (Bring Your Own Device)

3-month initial contract

Descripción de la vacante

Resilient is seeking a Data Science Lead to architect and deploy production-grade ML/AI solutions within a CI/CD/CT framework. You will drive end-to-end ML systems using Azure ML, Databricks, Spark, and robust governance to ensure models become scalable assets rather than notebook experiments.

You will lead a technical team, oversee model lifecycle, and collaborate with data engineers and business stakeholders to align KPIs with model objectives while ensuring reproducibility and auditability.

Formación

• 5–8 years total experience in data science with 4+ years in hands-on role
• 2+ years leading technical teams or complex projects
• BE/BS or MS/PhD in quantitative field required

Responsabilidades

• Architect end-to-end ML systems prioritizing MLOps for reproducibility and scalability
• Oversee full lifecycle development from hypothesis to production deployment
• Collaborate with Data Engineers and Stakeholders to translate KPIs into objective functions
• Automate data validation, model profiling, and hyperparameter tuning
• Establish monitoring for Data Drift and Concept Drift post-deployment

Conocimientos

Advanced Modeling

Mathematics

Deep Learning

Time Series Forecasting

A/B Testing

Statistical Inference

MLOps

Python

PySpark

SQL

Experiment Tracking

Educación

BE/BS or MS/PhD in Computer Science/Statistics/Math/Physics

Herramientas

Databricks

Azure ML

MLFlow

Kubeflow

Azure Pipelines

Docker

Kubernetes

Spark

Descripción del empleo

Resilient is seeking a Data Science Lead to architect and deploy production-grade ML/AI solutions within a CI/CD/CT framework. You will drive end-to-end ML systems using Azure ML, Databricks, Spark, and robust governance to ensure models become scalable assets rather than notebook experiments.

You will lead a technical team, oversee model lifecycle, and collaborate with data engineers and business stakeholders to align KPIs with model objectives while ensuring reproducibility and auditability.