🛠 Tech Stack

💼 About This Role

You'll architect end-to-end ML infrastructure and deploy high-impact models for a global AI-native technology company. You'll drive MLOps best practices across pipelines, serving, and monitoring and mentor engineering teams.

🎯 What You'll Do

• Architect end-to-end ML infrastructure across pipelines and serving
• Lead deployment of forecasting, optimization, and NLP models
• Design CI/CD workflows with Azure Pipelines, MLflow, Databricks
• Build monitoring systems for model drift and retraining automation

📋 Requirements

• 5-8+ years in ML Engineering or high-scale ML systems
• Deep expertise in Spark, Azure Databricks, MLflow, Kubernetes, Docker
• Proven track record deploying ML at enterprise scale with audit and monitoring
• Familiarity with hybrid/multi-cloud infrastructure

✨ Nice to Have

• AI tooling proficiency for workflow optimization
• Leadership experience in ML platform or DevOps teams
• Experience with feature stores, feature engineering, or AutoML

🎁 Benefits & Perks

• 🚀 High-Impact Environment
• 📚 Professional Development
• 🤝 Flexible and Collaborative Culture
• 🌍 Global Opportunities
• 🏢 Vibrant Community

📨 Hiring Process

Estimated timeline: 2-3 weeks · AI estimate

1. 1Recruiter Call· 30 min
2. 2Technical Screen· 45 min
3. 3On-site Interview· 3 hours