ML Ops Engineer

📋 Description

• Design and evolve production MLOps across the full ML lifecycle.
• Build systems for experiment tracking and artifact management.
• Develop reusable platform tooling and standards for faster delivery.
• Build infra for LLMs and agentic systems with prompts, traces, monitoring.
• Design evaluation and monitoring frameworks for AI systems.
• Build large-scale training pipelines for heterogeneous data sources.
• Write clean, modular production-grade Python services.
• Drive engineering quality with automated testing, CI/CD, and observability.

🎯 Requirements

• 5+ years in software, ML Ops, or ML platform engineering in production.
• Significant experience building/owning production ML infra and lifecycle systems.
• Strong Python with production-grade architecture, testing, packaging, error handling.
• Deep understanding of ML lifecycle: training, deployment, monitoring, retraining, lineage.
• Experience with Databricks, Spark, Delta Lake, or equivalent.
• Experience with MLflow, Metaflow, Kubeflow, or similar ML lifecycle tools.
• Design reusable workflow orchestration using Airflow, Metaflow, or Databricks.
• Familiar with LLMOps, AgentOps, and production AI systems.
• Strong English communication, written and verbal.

🎁 Benefits

• People-first culture and inclusive environment.
• Equal opportunity employer with focus on diversity.
• Safe, harassment-free workplace.
• Opportunity to shape Industrial AI at scale.