Machine Learning Operations Engineer
$160,000-$180,000 + Significant Equity, Benefits and Bonus
You will be working with a rapidly growing FinTech Startup Company that focuses on helping companies combat extra costs, overcharges, and duplicate charges. They are looking for an experienced Machine Learning Operations Engineer who has extensive experience working within a fast-paced environment and would like to collaborate heavily with their data science team to create a more scalable and reproducible machine learning environment.
As an MLOps Engineer, you will be required to:
- Experience with Post Deployment Models (AWS Sagemaker, Kubernetes Clusters or Similar)
- Monitoring Maintaining Models
- Cloud Platform Experience (AWS, GCP)
- Big Data Systems (Spark, Airflow)
- Automatically Retrain and Deploy Updated Models
- Monitor and Maintain Models Production Over Time
- Refactor Code From Data Scientist to Push Into Production
- Make Models Available As APIs
Your Skills and Experience
The successful MLOps Engineer will have the following skills and experience:
- Engineering background, that can manage engineers and set up engineering processes
- Proven track record taking ML solutions into productions
- Experience deploying Machine Learning Models to production
- Experience working within an AWS Environment
- Masters' minimum, likely Ph.D. (STEM)
- Well acquainted with big data engineering processes - Python, Spark, and ideally worked in MLflow/Kubeflow and Kubernetes or Docker
- Very strong on Machine Learning - create and build models uses ML libraries (scikit-learn, etc) and build models in Tensorflow, etc.
Data Science | Engineering | Natural Language Processing | Machine Learning | start-up | Python | FinTech | Software Engineering | Natural Language Understanding | Natural Language Generation
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