🛠 Tech Stack

💼 About This Role

You'll design, build, and deploy scalable ML models and pipelines across the full ML lifecycle. You'll collaborate with cross-functional teams to translate business problems into data-driven solutions and improve model performance. You'll work in a hybrid environment with a global team on cutting-edge ML systems.

🎯 What You'll Do

• Design, build, and deploy scalable ML models and pipelines
• Develop and optimize data processing workflows with Python, SQL, PySpark
• Deploy and monitor ML models in cloud environments
• Collaborate with cross-functional teams for data-driven solutions

📋 Requirements

• 5+ years of experience as a Machine Learning Engineer
• Strong proficiency in Python, SQL, and PySpark
• Hands-on experience with scikit-learn, TensorFlow, PyTorch, or XGBoost
• Experience deploying models in AWS, GCP, Azure, or Databricks

✨ Nice to Have

• Experience with MLOps practices like CI/CD and model versioning
• Experience with Kubernetes for ML workloads
• Ability to design evaluation metrics for model performance

🎁 Benefits & Perks

• 🏖️ Flexible work arrangement
• 💻 Hybrid work mode
• 🌍 Global company culture

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Screen· 30 min
2. 2Technical Interview· 60 min
3. 3Hiring Manager Interview· 45 min