ML Ops Engineer
augury
Job Description
- 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.