MLOps Engineer

Title: MLOps Engineer Location: Remote Client: Global newswire and media organization. Their content reaches more than half the global population daily. The tech org is modern and investing heavily in ML infrastructure to support large-scale media processing across text, image, and video. Role Description This is a production operations role - not a data science or modeling role. You'd be owning the full lifecycle of ML systems in production: deploying, scaling, monitoring, and governing inference pipelines so they run reliably, cost-effectively, and at scale. You're the person who makes sure what ML Engineers and Data Scientists build actually works in the real world - safely promoted across Dev, QA, and Prod, hitting SLAs, and not creating infrastructure headaches. The team runs hundreds of thousands of queries per day across text, image, and video pipelines - production stability and cost control are non-negotiable. You'll also be building the standard - establishing deployment patterns, containerization strategies, environment isolation, versioned rollouts, rollback mechanisms, and monitoring frameworks the org will run on going forward. Clear scope: you own deployment, infrastructure, monitoring, reliability, and cost governance. Model architecture and data science outputs stay with the ML and Data Science teams. Required Skills 5+ years of professional experience, including some experience working with large amounts of data, including text, images and videos. This is a must have. Should have experience working for either a media firm or a large, real-time environment where uptime is paramount. Hands-on production experience deploying and operating ML inference systems Strong AWS SageMaker experience - pipelines, endpoints, monitoring, multi-environment deployments Python - core to day-to-day work across pipelines and tooling PyTorch and TensorFlow from an ops/serving perspective (not modeling) BERT/transformer-based NLP models in a production context CI/CD pipeline experience - Jenkins and/or GitLab Containerized inference and autoscaling - model deployment and orchestration GPU/CPU compute selection, benchmarking, and optimization for production ML workloads Monitoring, alerting, drift detection, and A/B testing frameworks for ML in production Comfortable in a shared ownership model across ML, Data Science, DevOps, and Platform teams Nice to Have Computer vision or ranking/reranking systems experience Familiarity with ANN methods (HNSW, etc.) Experience running ML workloads over large-scale media datasets (text, image, video) Compensation Base salary up to $140,000.00 + 10% bonus target For additional information or to apply, please contact Bethany Moulthrop at bmoulthrop@talener.com Show more Show less