🛠 Tech Stack

💼 About This Role

You'll design, build, and deploy scalable machine learning 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. This role offers the opportunity to work on cloud-based ML systems at a global digital engineering company.

🎯 What You'll Do

• Design 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

📋 Requirements

• 5+ years of experience as an ML Engineer
• Proficiency in Python, SQL, and PySpark
• Hands-on with scikit-learn, TensorFlow, PyTorch, or XGBoost
• Experience with MLflow or similar for end-to-end pipelines

✨ Nice to Have

• Experience deploying models on AWS, GCP, Azure, or Databricks
• Understanding of MLOps practices (CI/CD, model versioning, automated retraining)
• Experience with Kubernetes for ML workloads

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Call· 30 min
2. 2Technical Interview· 60 min
3. 3Team Fit Interview· 45 min

🚩 Heads Up

• Job summary mentions software development but role is ML Engineer; some responsibility mismatch