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sql python pandas tensorflow pytorch📋 Description
- Design, develop, and deploy production-grade ML solutions.
- Build scalable ML pipelines and infrastructure.
- Optimize model performance, reliability, and efficiency.
- Write clean, maintainable, production-quality code.
- Conduct rigorous experimentation and model evaluation.
- Troubleshoot and resolve complex technical challenges.
🎯 Requirements
- ML Fundamentals: supervised, unsupervised, and reinforcement learning
- Feature engineering, training, evaluation, tuning, validation
- ML Frameworks: TensorFlow, PyTorch, or similar
- Deep Learning: CNNs, RNNs, Transformers
- LLMs & Generative AI: production LLM apps, prompts, RAG, vector DBs
- Python, Pandas, NumPy, SQL; ETL/ELT; Spark
- MLOps & Production: deployment, Docker, CI/CD, monitoring, experiment tracking
- Cloud & Infrastructure: AWS SageMaker, Lambda; GCP ML; IaC (Terraform/CloudFormation)
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