
Machine Learning Engineer
2 days ago
As a key member of our team, you will be responsible for designing and implementing end-to-end machine learning pipelines that drive business outcomes.
- Design and implement scalable and efficient ML workflows for model training, evaluation, and deployment.
- Collaborate with cross-functional teams to operationalize ML models using TensorFlow Serving, TorchServe, and other MLOps tools.
- Develop and maintain CI/CD pipelines for ML workflows to ensure seamless model updates and versioning.
- Implement monitoring and logging solutions to track ML model performance and optimize infrastructure for performance, scalability, and cost-efficiency.
To succeed in this role, you should possess:
- Strong programming skills in Python, with experience in popular ML frameworks like scikit-learn, TensorFlow, and PyTorch.
- Expertise in containerization technologies (Docker) and orchestration platforms (Kubernetes).
- Proficiency in cloud platforms (AWS) and their ML-specific services, including SageMaker and Rekognition.
- Strong understanding of DevOps practices and tools, such as GitLab, Artifactory, and Gitflow.
- Experience with monitoring and observability tools (Prometheus, Grafana, ELK stack) and knowledge of distributed training techniques.
We recognize the importance of flexible work arrangements and offer a hybrid work environment that supports a healthy work-life balance. Our focus is on career growth and professional development, providing opportunities for certification and training in the latest technologies, including MLOps and Machine Learning. We also prioritize collaboration and teamwork, with regular check-ins and feedback sessions to ensure your success in this role.
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