ML Ops / Data Engineer

Job description

ML Ops / Data Engineer Role Overview: We’re hiring an ML Ops Engineer / Data 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: you’ll design and operate data pipelines that feed models, and you’ll build the tooling that trains, deploys, monitors, and retrains them. You’ll 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 You’ll 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: Building and maintaining batch and near–real-time data pipelines Ensuring data quality, schema evolution, and li...