
Chief Machine Learning Pipeline Architect
1 day ago
We are seeking an experienced Machine Learning Pipeline Developer to join our team. As a key member of our Data Science organization, you will play a critical role in bridging the gap between machine learning development and production deployment.
In this challenging and rewarding position, you will work closely with data scientists, software engineers, and IT operations to ensure that ML models are deployed, monitored, and scaled effectively. You will design, develop, and implement CI/CD pipelines for automating the deployment of machine learning models to production environments.
Key Responsibilities:- Design and Implement CI/CD Pipelines: Develop and deploy automated workflows for model training, testing, and deployment.
- Build Model Monitoring Tools: Create tools to track model performance in real-time and trigger automatic re-training when model drift occurs.
- Collaborate with Cross-Functional Teams: Work closely with data scientists, software engineers, and business stakeholders to ensure smooth integration of ML models into production.
- Manage Cloud Resources: Ensure high availability, cost optimization, and efficient scaling of ML workloads on cloud platforms like AWS, GCP, or Azure.
- Implement Versioning Systems: Design and implement version control systems for model artifacts, datasets, and training environments.
- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.
- 3+ years of experience in deploying and maintaining machine learning models in production environments.
- Expertise in programming languages such as Python, R, or Java.
- Strong understanding of machine learning workflows and tools like TensorFlow, PyTorch, Scikit-learn, or similar.
- Experience with containerization and orchestration technologies (Docker, Kubernetes).
- Proficiency in cloud infrastructure, CI/CD, and DevOps.
- Advanced knowledge of ML frameworks like Kubeflow, Seldon, or TFX for automating ML pipelines.
- Experience with experiment tracking platforms (e.g., Weights & Biases).
- Knowledge of techniques for explaining and interpreting machine learning models (e.g., SHAP, LIME).
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