10 publicaciones de empleo destacadas de mlops engineer en ciudad de méxico, dif (¡Están contratando!)

Data Engineer
PROGRAMMING.COM —Azcapotzalco, CDMX
- Tiempo completo
- Home Office (Desde casa)
- Azure
- SQL
- Disponible para viajar
Postulación rápida
3d
ML Ops Engineer S.Sr
Ucloud —Ciudad de México, CDMX
- Python
Postulación rápida
Ingeniero de Inteligencia Artificial
Capital Empresarial Horizonte —Benito Juárez, CDMX
- $50,000 a $85,000 por mes
- Tiempo completo
- Azure
- DevOps
- Inglés
Postulación rápida
Data Engineer
NEXU —Ciudad de México, CDMX
- Tiempo completo
- Azure
- DevOps
- Inglés
Postulación rápida
Data Engineer
Orion Innovation —Ciudad de México, CDMX
- Trabajo híbrido
- Español
- DevOps
- Ingeniería
Postulación rápida
0152 - Data & Analytics Engineer Mid
ALTEN MÉXICO —Ciudad de México, CDMX
- Tiempo completo
- Trabajo híbrido
- Ingeniería
- Inglés
- SQL
Postulación rápida
ML Ops Engineer Sr
Ucloud —Ciudad de México, CDMX
- Experiencia en manufactura
- Python
Postulación rápida
Sr. Devops Engineer
Monks —Ciudad de México, CDMX
- Home Office (Desde casa)
- Administración de personal
- Informática
- DevOps
Postulación rápida
Cloud Architect
HSBC Global Services Limited —Azcapotzalco, CDMX
- Tiempo completo
- Azure
- Administración de personal
- DevOps
3d
Consultor Databricks Senior
NetPartners International —Ciudad de México, CDMX
- Español
- Azure
- DevOps
Postulación rápida

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Role: Data Engineer

Location: Remote (Requires occasional travel)

Responsibilities:

Build and operate robust data pipelines for ingestion, cleaning, and transformation using Databricks, Airflow, or Dagster.
Develop efficient ETL/ELT workflows in Python and SQL to support both batch and streaming workloads.
Collaborate with ML and AI teams to deliver high-quality datasets for training, evaluation, and production features.
Model and maintain structured data assets (Delta, Parquet, Iceberg) for reliability, versioning, and lineage tracking.
Implement orchestration and monitoring — schedule jobs, track dependencies, and automate recovery from failures.
Ensure data quality and compliance through validation frameworks, schema enforcement, and audit logging.
Contribute to data platform evolution — evaluate tools, standardize best practices, and improve developer experience.
Support performance and cost optimization across compute, storage, and orchestration systems.

Qualifications:

3–6 years of experience as a Data Engineer or ETL Developer in a production environment.
Proficiency in Python and SQL; strong familiarity with Databricks, Spark, or equivalent big-data frameworks.
Experience with workflow orchestration tools such as Airflow, Dagster, Luigi or Prefect.
Deep understanding of data modeling, data warehousing, and distributed data processing.
Knowledge of modern data lakehouse architectures (Delta, Parquet, Iceberg).
Familiarity with CI/CD, GitHub Actions, and data pipeline testing frameworks.
Comfort working in a cross-functional environment with ML, product, and analytics teams.

Nice to Have:

Experience with sports, telemetry, or sensor data pipelines.
Familiarity with streaming frameworks (Kafka, Spark Structured Streaming, Flink).
General knowledge of American football, the NFL, and college football
Background in data governance, lineage, and observability tools (Monte Carlo, Great Expectations, Unity Catalog, OpenLineage).
Experience with cloud infrastructure (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
Exposure to best practices in machine-learning model management and MLOps