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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