Have Master's degree in the field of AI / ML and data science with proven ability to design and develop models
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.