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- 6+ years of experience in data science and machine learning, with at least 3+ years in ML engineering roles.
- Proven experience in end-to-end ML lifecycle: data wrangling, model development, deployment, and monitoring.
- Strong programming skills in Python (pandas, scikit-learn, TensorFlow/PyTorch, etc.).
- Strong knowledge in NoSQL databases (any experience in Graph database is desirable)
- Experience with MLOps tools: MLflow, TFX, Airflow, Kubeflow, or similar.
- Familiarity with cloud platforms (GCP, AWS, or Azure) for ML deployment.
- Knowledge of data science techniques including supervised/unsupervised learning, NLP, time series, etc.
- Experience with CI/CD pipelines and containerization (Docker, Kubernetes).
- Strong understanding of AI governance, model risk management, and regulatory requirements in AI.
- Ability to communicate technical concepts to non-technical stakeholders.
Seniority level
Employment type
Job function
- Information Technology
- Industries: IT Services and IT Consulting
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