MLOps Engineer

📋 Description Design and evolve production MLOps capabilities across the full ML lifecycle including datasets Build systems for experiment tracking, artifact management, reproducibility, versioning, lineage Develop reusable platform tooling, golden paths, and engineering standards that improve consistency Build operational infrastructure for LLM and agentic systems including prompts, tools, traces Design evaluation and monitoring frameworks for AI systems including answer quality, latency Build and optimize large-scale training pipelines supporting heterogeneous data sources and 🎯 Requirements 5+ years of professional software engineering, MLOps, or ML platform engineering experience in Significant experience building or owning production ML infrastructure and lifecycle systems. Strong Python engineering skills with production-grade architecture, modular design, testing Strong understanding of the end-to-end ML lifecycle including training, deployment, monitoring Experience working with large-scale data platforms such as Databricks, Spark, Delta Lake, or Experience with ML platform and MLOps frameworks such as MLflow, Metaflow, Kubeflow, or equivalent