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AIML Engineer

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charlotte, nc • Posted 5 days ago
Hybrid Full Time Architecture and Engineering Occupations
AIML Engineer

Max Bill Rate: $70

Experience: 10 to 13 yrs

Job Summary: Familiarity with cloud based services, containerization (e.g., Docker), and server deployment. Solid understanding of software development principles, version control systems, and continuous integration/continuous deployment (CI/CD) pipelines. Work in a hybrid model-2-3 days a week.

Roles & Responsibilities
  • NLP Model Development: Design and implement state-of-the-art NLP models and algorithms for various text/image/video files classification tasks.
  • Preprocess and transform raw text data into suitable numerical representations, applying techniques such as tokenization, stop word removal, and TF-IDF to extract meaningful features.
  • Model Training and Evaluation: Train and fine-tune NLP models using large-scale datasets, and evaluate their performance using appropriate metrics like accuracy, precision, recall, Fl-score, and ROC curves.
  • Model Deployment: Deploy NLP models in production environments, ensuring scalability, efficiency, and robustness. Integrate the models with production servers, APIs, and web services for seamless end-to-end functionality.
  • Monitoring and Maintenance: Implement logging and monitoring mechanisms to track model performance and behavior in real-time.
  • Proactively identify and resolve any issues or errors that arise during deployment.
  • Performance Optimization: Continuously optimize model inference times, memory usage, and resource consumption to achieve optimal performance and responsiveness in production servers.
  • Data Management: Collaborate with data engineers to ensure the availability, quality, and reliability of data used for training and inference.
  • Manage data versioning and storage in compliance with best practices and privacy regulations.
  • Strong proficiency in programming languages such as Python, along with libraries like TensorFlow, PyTorch, scikit-learn, and NLTK.
  • Proven experience in developing and deploying NLP models for text classification tasks in real-world applications.
  • Knowledge of deep learning architectures, transformer models, and word embeddings for NLP.
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