Senior ML Engineer

📋 Description

• Own end-to-end ML lifecycle from framing to retraining with minimal guidance.
• Design and architect production-grade ML systems with contracts and infra patterns.
• Build real-time and batch ML pipelines with Airflow/Prefect or cloud-native tools.
• Operationalize models: containerize, version, and deploy to scalable serving infra.
• Implement robust MLOps: CI/CD, evaluation gates, canary/shadow deployments, tracking.
• Design feature pipelines and feature stores to ensure data quality and freshness.

🎯 Requirements

• 5-7 years of hands-on ML engineering with at least 3 end-to-end production deployments.
• Production-grade Python: clean, modular, typed, tested code.
• Hands-on MLOps: CI/CD, automated eval pipelines, versioning, experiments.
• Deep expertise with at least one cloud ML platform: SageMaker, Vertex AI, or Azure ML.
• Expert SQL for complex data transforms on BigQuery/Snowflake/Redshift.
• Feature engineering in production: feature stores, skew mitigation, leakage prevention.
• Model monitoring and observability: drift, latency, retraining triggers.

🎁 Benefits

• Competitive salary
• Strong insurance package
• Extensive learning and development resources