
Machine Learning Operations Professional
23 hours ago
This role requires a high level of expertise in machine learning operations, including deployment, monitoring, and scaling of models on cloud platforms such as AWS or GCP. Experience with continuous integration/continuous deployment pipelines for ML workflows using GitHub Actions or Jenkins is essential.
Key Responsibilities:
- Model Deployment and Management: Deploy, monitor, and scale machine learning models on cloud platforms.
- Pipeline Management: Build and maintain continuous integration/continuous deployment pipelines for ML workflows.
- Containerization and Orchestration: Containerize and orchestrate workloads with Docker and Kubernetes.
- Model Registry and Observability: Implement model registries, feature stores, and observability using MLflow and Prometheus/Grafana.
- Collaboration with Data Scientists: Collaborate with data scientists to operationalize ML models for personalization, recommendations, NLP.
- Programming Skills: Proficient in Python, ML frameworks (Scikit-learn, TensorFlow, PyTorch), and data pipelines (Airflow, Spark, SQL).
- Bonus Experience: Bonus experience with real-time ML serving, A/B testing, or media/recommender systems.
Nice-to-Have Qualifications:
- Experience with subscription, media, or recommender systems.
- Knowledge of experiment design and causal inference.
- Exposure to real-time ML serving (KFServing, Seldon, Ray Serve).
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