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