MLOps Engineer

This hybrid role combines MLOps and data engineering to build robust, reproducible, and automated pipelines that support the end-to-end lifecycle of machine learning systems.

What You’ll Be Doing

• Build ETL/ELT pipelines to ingest and transform data for ML training
• Automate model retraining, deployment, and monitoring processes
• Enable data and model version control
• Integrate model logging, exception handling, and alerting systems
• Maintain reproducibility, data lineage, and audit trails
  
  

What We’d Love To See

• 3–6 years in data engineering or MLOps workflows
• Airflow, DBT, Apache Kafka/Spark, SQL, Python
• Docker, Kubernetes, MLflow, Prometheus/Grafana
  
  

It’d Be Great If You Had

• GCP BigQuery, S3, Redshift, Snowflake