MLOps Engineer
Our mission is to transform how people and machines work together to push the boundaries of human productivity. A leader in Industrial AI, Augury helps the world’s manufacturers leverage real-time production insights to drive new levels of efficiency. Combining predictive and prescriptive AI technology with industry expertise, production teams can proactively address alerts, minimize downtime, reduce asset costs, and maximize yield and capacity. Our customers achieve payback in six months or less, enabling global scale. We're looking for team members excited to partner with the world's manufacturers and build the future of production together.As an MLOps Engineer at Augury, you will help build and scale the production platform behind Augury’s Industrial AI Workforce, enabling teams across the company to develop, evaluate, deploy, and operate ML and AI systems consistently and safely. A Day In Your Life Design and evolve production MLOps capabilities across the full ML lifecycle including datasets, features, models, evaluations, deployments, monitoring, retraining, and feedback signals. Build systems for experiment tracking, artifact management, reproducibility, versioning, lineage, promotion workflows, and production readiness. Develop reusable platform tooling, golden paths, and engineering standards that improve consistency and delivery velocity across teams. Build operational infrastructure for LLM and agentic systems including prompts, tools, traces, evaluations, observability, safety boundaries, and production monitoring. Design evaluation and monitoring frameworks for AI systems including answer quality, latency, grounding, reliability, and operational regressions. Build and optimize large-scale training pipelines supporting heterogeneous data sources and scalable compute patterns. Write clean, modular, production-grade Python services and platform libraries. Drive engineering quality through automated testing, CI/CD, observability, deployment standards, and operational best practices. What You Bring 5+ years of professional software engineering, MLOps, or ML platform engineering experience in production environments. Significant experience building or owning production ML infrastructure and lifecycle systems. Strong Python engineering skills with production-grade architecture, modular design, testing, packaging, and robust error handling. Strong understanding of the end-to-end ML lifecycle including training, deployment, monitoring, retraining, reproducibility, and lineage. Experience working with large-scale data platforms such as Databricks, Spark, Delta Lake, or equivalent ecosystems. Experience with ML platform and MLOps frameworks such as MLflow, Metaflow, Kubeflow, or equivalent ML lifecycle-management systems. Proven ability to design reusable workflow orchestration using Airflow, Metaflow, or Databricks, covering automation, scheduling, dependency management, and production reliability. Familiarity with operational patterns for ...