over 1 year ago
Remote, United StatesMid Level
Responsibilities
- Implement and maintain CI/CD pipelines for deploying machine learning models to production.
- Design and manage scalable infrastructure for machine learning training, testing, and serving.
- Automate data preprocessing, model training, and deployment workflows.
- Monitor deployed models and systems, resolve issues proactively, and optimize inference latency, scalability, and resource utilization.
- Maintain version control and governance for datasets, models, and code.
- Collaborate with data scientists, software engineers, product teams, DevOps teams, and cloud engineering teams.
- Recommend and implement improvements to MLOps processes and infrastructure.
Requirements
- Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field, or an equivalent combination of education and experience.
- 2–3 years of hands-on experience in MLOps, DevOps, or related roles.
- Experience with MLflow, Kubeflow, or SageMaker and with feature stores and model versioning systems.
- Experience building CI/CD pipelines using Jenkins, GitLab CI, or similar tools.
- Proficiency in Python and familiarity with Docker, Kubernetes, distributed computing frameworks such as Apache Spark, and cloud platforms such as AWS, Azure, or Google Cloud.
- Knowledge of model monitoring, logging, debugging, database technologies, and SQL, NoSQL, and ETL/ELT data pipelines.
- Strong problem-solving, detail orientation, communication, and collaboration skills.
Tech Stack
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