AIML Engineer

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.

• 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 Tensor Flow, 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.