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Develop and deploy advanced machine learning and deep learning models for real-world applications
Build and refine high-quality datasets, ensuring robustness and relevance
Conduct data analysis to guide model development and validate performance
Manage the end-to-end ML lifecycle — from training and tuning to validation and deployment
Implement MLOps practices (CI/CD, monitoring, automation)
Work with Python and core AI/ML libraries (e.g., PyTorch, TensorFlow, scikit-learn)
Containerize models using Docker for scalable deployment
For LLM applications: design and test prompts, evaluate outputs, and support data labeling and fine-tuning
Collaborate with Product Owners, Software Engineers, and QA teams to integrate AI models into production Proven experience developing and deploying ML models in production
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