Senior MLOps Engineer

Are you passionate about AI? 🤖 At Satori Analytics, we aim to change the world one algorithm at a time by bringing clarity to global brands through Data & AI. From cloud-based ecosystems for fintech to predictive models for airlines, our cutting-edge solutions cover the entire data lifecycle—from ingestion to AI applications. As a fast-growing scale-up, our team of 100+ tech specialists—including Data Engineers, Data Scientists, and more—delivers innovative analytics solutions across industries like FMCG, retail, manufacturing and FSI. Join us as we lead the data revolution in South-Eastern Europe and beyond! We’re looking for a Senior MLOps Engineer to help build the infrastructure behind production AI: the pipelines, platforms, deployment patterns, and observability that allow models to move from experimentation to real business value. What Your Day Might Look Like: • Make AI production-ready: Design and maintain the infrastructure that takes ML models from experimentation to reliable, scalable deployment. • Build automated ML pipelines: Create repeatable workflows for training, evaluation, deployment, and retraining — with versioning and reproducibility built in. • Deploy and serve models: Package models as production services across cloud, on-premise, or hybrid environments, with performance and reliability in mind. • Monitor what matters: Track model performance, data drift, system health, and production signals to support better retraining and troubleshooting decisions. • Enable AI teams: Work closely with Data Scientists, AI Engineers, and Software Engineers to improve how models are tested, deployed, and maintained. • Set the standard: Contribute to best practices around CI/CD, model registry, observability, security, and governance. • Support GenAI at scale: Help deploy and optimize LLM-based systems, including inference services, GPU usage, and RAG infrastructure where needed. • Keep systems secure and reliable: Ensure ML deployments follow strong practices around access control, data governance, and operational resilience.