Senior Machine Learning DevOps Engineer
Skills
- Experience with ML model deployment and evaluation using tools such as Arize, Arize Phoenix, MLFlow.
- Proficiency with Docker, Kubernetes, AWS Lambda/ECS, log aggregation, CI/CD, Linux, and Terraform.
- Proficient in Python, TensorFlow, PyTorch, scikit-learn, and ElasticSearch.
- Experience with AWS services including SageMaker, Athena, Fargate, Bedrock, CloudWatch, S3, ECS, EMR, EC2, and Lambda.
- Configuring access, monitoring, and alerts for computational resources and model performance.
Responsibilities
- Architect and design end-to-end ML pipelines and CI/CD pipelines.
- Implement automated ML pipelines including data preprocessing, model training, and deployment.
- Implement monitoring systems for model performance, data drift, and system health.
- Identify and resolve issues to maintain high availability and reliability of ML services.
- Implement new tools and technologies to enhance ML infrastructure.
- Establish data governance policies to protect sensitive information.
- Mentor junior ML engineers and collaborate with cross-functional teams.
Education
- Master's degree (or equivalent foreign degree) in Computer Science, Business Analytics, Machine Learning or related field with 3 years of relevant experience, or
- Bachelor's degree (or equivalent foreign degree) in Computer Science, Business Analytics, Machine Learning or related field with 5 years of relevant experience.
Benefits
- Medical, dental, vision, life, disability insurance, and Employee Assistance Program.
- 401K retirement plan with company match, flexible spending and health savings account.
- Up to 184 hours (23 days) of PTO per year plus company holidays.
- Up to 14 weeks of paid parental leave.
- Pet insurance.
Average salary estimate
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