Chess.com
Global
Site Reliability Engineer
Devops Full-time Mid-Senior level 1h ago
- Python
- Nosql
- GCP
- ELK
AI Summary
Seeking an MLOps Engineer to bridge Data Science and Infrastructure teams. Responsibilities include MLOps pipeline creation, CI/CD, cloud provisioning, and Kubernetes orchestration. Requires 2+ years of MLOps/DevOps experience, AWS knowledge, and Python/Bash proficiency.
Key Highlights
MLOps pipeline creation on AWS
CI/CD and Kubernetes orchestration
2+ years MLOps/DevOps experience
Key Responsibilities
Assist in designing, implementing, and maintaining scalable MLOps pipelines on AWS
Coordinate with platform team to troubleshoot Kubernetes clusters
Develop and maintain CI/CD pipelines for model and application deployment
Collaborate closely with Data Science, and DevOps team
Implement security best practices
Monitor, troubleshoot, and optimize data and ML pipelines
Set up and manage model monitoring systems
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Technical Skills Required
Benefits & Perks
Attractive remuneration package plus performance related reward
Private health insurance
Corporate pension fund
Intellectually stimulating work environment
Continuous personal development and international training opportunities
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Nice to Have
Experience working with serverless architectures
Familiarity with advanced Kubernetes concepts
Experience with Data Engineering pipelines
Experience with ML frameworks like TensorFlow, PyTorch and Keras
Experience with ML platforms like Kubeflow and/or SageMaker
Experience with workflow engines like Argo Workflows and/or Airflow
Job Description
The Role:
We are looking for a skilled MLOps Engineer to join our team and play a key role in bridging the gap between our Data Science and Infrastructure teams. You will be responsible for supporting the Data Science team in MLOps-related tasks while also helping in DevOps initiatives, including CI/CD pipeline creation, provisioning of cloud resources using tools like Terraform, and Kubernetes orchestration. You will collaborate closely with data scientists and engineers to deploy data pipelines, train machine learning models, and manage their deployment within scalable cloud environments while ensuring high performance, security, and reliability throughout the ML lifecycle.
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