Job Responsibilities

• Design, develop, and deploy NLP models for text processing, semantic extraction, and classification tasks.
• Implement and optimize text representation techniques such as n-grams, bag of words, sentiment analysis, and other NLP methodologies.
• Collaborate with MLOps teams to streamline the development, deployment, and maintenance of machine learning models.
• Develop robust, scalable, and testable Python code to implement NLP algorithms and processes.
• Build and maintain Kubernetes-based infrastructure for model deployment, scaling, and monitoring.
• Ensure optimal performance of NLP models by leveraging statistics, classification algorithms, and other advanced machine learning techniques.
• Contribute to software architecture design with a focus on efficiency, scalability, and maintainability.
• Troubleshoot, debug, and optimize NLP solutions for improved accuracy and performance.
• Continuously improve processes and adopt best practices for integrating machine learning workflows.

Required Skills & Qualifications

• Proven experience as an NLP Engineer or in a similar role.
• Strong understanding of NLP techniques, text representation methods (e.g., n-grams, bag of words, sentiment analysis), and classification algorithms.
• Proficiency in Python programming, with a focus on writing clean, efficient, and testable code.
• Experience in working with Kubernetes for deploying and scaling machine learning models.
• Familiarity with MLOps best practices for model lifecycle management (e.g., model versioning, monitoring, and retraining).

Preferred Skills

• Experience working with machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn.
• Knowledge of cloud platforms (e.g., AWS, GCP, Azure) and their ML-related services.
• Familiarity with containerization technologies (Docker, Kubernetes) and CI/CD pipelines for model deployment.

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