MLOps Engineer (Pytorch, Tensorflow, Model Deployment) *** Direct client ***
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Job Description: We are seeking a skilled and experienced Machine Learning (ML) Engineer to join our team. The ideal candidate will have a strong understanding of the engineering and operations side of ML, and will be responsible for setting up best practices for deploying models at scale. They will also handle monitoring, troubleshooting, and optimizing these models. This role involves close collaboration with scientists to improve access and use of heterogeneous datasets.
Key Responsibilities:
Develop and implement best practices for deploying ML models at scale.
Monitor, troubleshoot, and optimize ML model performance.
Collaborate with scientists to improve data accessibility and usability for diverse datasets.
Write efficient and scalable code using frameworks such as PyTorch and TensorFlow.
Assist in integrating scientific algorithms and directions dictated by research scientists.
Required Qualifications:
Proven experience in ML engineering and operations.
Proficiency in deploying, monitoring, and optimizing ML models.
Strong coding skills in PyTorch, TensorFlow, and other relevant ML frameworks.
Experience working with heterogeneous datasets.
Ability to collaborate effectively with scientists and understand scientific algorithms.
Preferred Qualifications:
Experience with cloud-based ML deployment (AWS, Google Cloud Platform, Azure).
Familiarity with containerization and orchestration tools (Docker, Kubernetes).
Knowledge of data engineering and pipeline tools (Apache Spark, Apache Kafka).
Machine Learning Engineer
ML Engineering
Deploying ML Models
PyTorch
TensorFlow
Data Science
Heterogeneous Datasets
ML Model Optimization
Cloud ML Deployment
Scientific Algorithms
Data Engineering
Monitoring ML Models
ML Operations
Containerization
Orchestration
