ML Ops Engineer
Role Overview
Were hiring an ML Ops Engineer to own the reliability, scalability, and operational integrity of our machine-learning systems in research & production. This role sits at the intersection of data engineering and ML infrastructure: youll design and operate data pipelines that feed models, and youll build the tooling that trains, deploys, monitors, and retrains them.
Youll work closely with research engineers and product teams, taking models from experimentation to production-grade systems with clear SLAs, reproducibility guarantees, and observable behaviour. This is not a research role; it is a hands-on engineering role focused on making ML systems work reliably at scale.
What Youll Work On
ML lifecycle infrastructure
* Productionizing models: packaging, deployment, versioning, and rollback
* Designing CI/CD pipelines for ML (training ? validation ? deployment)
* Implementing model monitoring (data drift, prediction drift, performance decay)
* Managing experiment tracking and reproducibility
Data engineering foundations
1. Building and maintaining batch and nearreal-time data pipelines
2. Ensuring data quality, schema evolution, and lineage across systems
3. Designing datasets and feature pipelines that support both training and in...