ML Ops Engineer

Requirements 3–7 years of professional experience in ML Ops, Data Engineering, or adjacent backend roles Strong production Python skills (clean APIs, testing, performance awareness) Experience deploying and operating ML models in production environments Solid understanding of: Model training vs. inference requirements Batch vs. streaming data pipelines Failure modes in data-driven systems Hands‐on experience with at least one modern orchestration or workflow system Comfort working with cloud infrastructure and containerized workloads Ability to reason about system design, not just tool usage (Desirable) Experience operating systems at TB-scale data volumes or higher (Desirable) Prior ownership of model monitoring, drift detection, or automated retraining (Desirable) Familiarity with feature stores or online/offline feature consistency problems (Desirable) Experience supporting multiple models or teams on a shared ML platform (Desirable) Exposure to regulated or high‐reliability production environments What the job involves We're 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: 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 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 Building and maintaining batch and near‐real‐time data pipelines Ensuring data quality, schema evolution, and lineage across systems Designing datasets and feature pipelines that support both training and inference Operating pipelines with clear reliability and latency expectations Defining and meeting availability, latency, and freshness targets for ML services Debugging production issues across data, infrastructure, and model layers Improving system robustness through automation and observability Collaborating with platform and security teams on access, secrets, and compliance Writing production‐grade Python used in long‐running services and pipelines Establishing testing, validation, and release practices for ML systems Making trade‐offs explicit between research flexibility and production stability

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