Machine Learning Engineer

Tasks

• Convert data science prototypes into production-ready ML models, services, and pipelines.
• Develop reproducible model training and inference workflows with automated testing and version control.
• Build scalable online and offline model-serving systems, optimizing latency, throughput, and batch processing.
• Implement MLOps solutions for model tracking, registry, deployment, monitoring, and lifecycle management.
• Collaborate with data engineering teams on feature pipelines, data quality, and training-serving consistency.
• Monitor model drift, performance degradation, and production health; implement rollback and incident-response strategies.

Requirements

• Strong Python programming and hands-on experience with TensorFlow or PyTorch.
• ML model deployment, inference APIs, and production ML system design.
• Docker, Kubernetes, REST APIs, and CI/CD for ML workloads.
• Knowledge of model reproducibility, data leakage, training-serving skew, and drift detection.
• Need local or Nearby

Schedule

• Long Term Contract

How to apply

• Share resume by email

Location

• New York, NY