Posted:1 month ago|
Platform:
Work from Office
Full Time
Key Responsibilities: Design and develop machine learning and deep learning models for tasks such as text classification , entity recognition , sentiment analysis , and document intelligence . Build and optimize NLP pipelines using models like BERT , GPT , LayoutLM , and Transformer architectures . Implement and experiment with Generative AI techniques using frameworks like Hugging Face , OpenAI APIs , and PyTorch/TensorFlow . Perform data collection, web scraping , data cleaning , and feature engineering for structured and unstructured data sources. Deploy ML models using Docker , Kubernetes , and implement CI/CD pipelines for scalable and automated workflows. Use cloud services (e.g., GCP , Azure AI ) for model hosting, data storage, and compute resources. Collaborate with cross-functional teams to integrate ML models into production-grade applications. Apply MLOps practices including model versioning, monitoring, retraining pipelines, and reproducibility. Technical Skills: Languages & Libraries: Python, Pandas, NumPy, Scikit-learn, TensorFlow, Keras, PyTorch, OpenCV, Seaborn, XGBoost, NLTK, Hugging Face, BeautifulSoup, Selenium, Scrapy Modeling & NLP: Logistic Regression, Random Forest, SVM, CNN, RNN, Transformers, BERT, GPT, LLMs Tools & Platforms: Git, Docker, Kubernetes, CI/CD, Azure AI Services, GCP, Vertex AI Concepts: Machine Learning, Deep Learning, MLOps, Generative AI, Text Analytics, Predictive Analytics Databases & Querying: Basics of SQL Other Skills: Data Visualization (Matplotlib, Seaborn), Model Optimization, Version Control
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