Master’s degree or foreign equivalent in Computer Science, Data Science, Computer Engineering or related field and 3 years of experience in the job offered or related occupation. 3 years of experience with each of the following skills is required: 1. Developing NLP pipelines in Python using at least 2 of the following: NLTK, spaCy, Gensim, or HuggingFace. 2. Implementing text preprocessing workflows, creating feature extraction algorithms, and building and training models with scikit-learn, TensorFlow, or PyTorch. 3. Developing reusable modules for NLP and writing production-ready code. 4. Querying large datasets in SQL to extract textual information. 5. Designing database schemas optimized for NLP applications. 6. Writing complex queries to join structured and unstructured data sources. 7. Creating ETL processes for text data, optimizing query performance for large text corpora, and implementing database operations in analytics pipelines. 8. Applying supervised Machine Learning techniques to NLP problems, implementing unsupervised methods for text analysis, evaluating model performance with appropriate metrics, building ensemble models, conducting hyperparameter optimization, and applying transfer learning with pre-trained embeddings. 9. Deploying NLP models and pipelines on Google Cloud Platform (GCP) infrastructure. 10. Utilizing AI Platform for training and serving ML models. 11. Managing data storage with Cloud Storage, BigQuery, or Cloud SQL. 12. Implementing data processing pipelines with Dataflow or Dataproc. 2 years of experience with each of the following skills is required: 1. Managing code versioning for collaborative NLP model development, implementing code review processes, and resolving merge conflicts in multi-developer environments. 2. Using Git for CI/CD integration with model deployment and organizing repositories for maintainable ML codebases. 1 year of experience with each of the following skills is required: 1. Using Cloud Functions for serverless text processing and monitoring model performance. 2. Containerizing NLP applications for deployment, creating and managing deployment configurations, and setting up routes and services with OpenShift. 3. Implementing resource allocation and scaling strategies, configuring persistent storage for models and data, and managing deployments with rolling updates. 4. Fine-tuning pre-trained Large Language Models (BERT, GPT, or T5) for domain-specific tasks. 5. Implementing prompt engineering techniques and evaluating LLM outputs for accuracy. 6. Creating embeddings for semantic search, optimizing inference for production, and reducing hallucinations and improving factuality. 7. Building CI/CD pipelines in Tekton for NLP model deployment. 8. Creating reusable pipeline components for text processing, managing workflow triggers, and implementing testing and validation steps. 9. Configuring resource requirements, integrating model evaluation metrics, and setting up automated retraining pipelines.
