What you'll do:

• Model Development and Optimization: Design, build, and deploy NLP models, including transformer models (e.g., BERT, GPT, T5) and other SOTA architectures, as well as traditional machine learning algorithms (e.g., SVMs, Logistic Regression) for specific applications.
• Data Processing and Feature Engineering: Develop robust pipelines for text preprocessing, feature extraction, and data augmentation for structured and unstructured data.
• Model Fine-Tuning and Transfer Learning: Fine-tune large language models for specific applications, leveraging transfer learning techniques, domain adaptation, and a mix of deep learning and traditional ML models.
• Performance Optimization: Optimize model performance for scalability and latency, applying techniques such as quantization, ONNX formats etc.
• Research and Innovation: Stay updated with the latest research in NLP, Deep Learning, and Generative AI, applying innovative solutions and techniques (e.g., RAG applications, Prompt engineering, Self-supervised learning).
• Stakeholder Communication: Collaborate with stakeholders to gather requirements, conduct due diligence, and communicate project updates effectively, ensuring alignment between technical solutions and business goals.
• Evaluation and Testing: Establish metrics, benchmarks, and methodologies for model evaluation, including cross-validation, and error analysis, ensuring models meet accuracy, fairness, and reliability standards.
• Deployment and Monitoring: Oversee the deployment of NLP models in production, ensuring seamless integration, model monitoring, and retraining processes.

What makes you a great fit:

• Education: Bachelor's degree in computer science, or a related field.
• Experience: Minimum of 3 to 5 years of experience in NLP, Deep Learning, and ML, with a proven track record of developing and optimizing both LLM and traditional machine learning NLP models.
• Advanced NLP Techniques: Proficiency in transformer models (e.g., BERT, RoBERTa, GPT, T5), and experience with techniques like entity recognition (NER), text classification, summarization, question answering, and language generation.
• Programming: Strong programming skills in Python, with experience in NLP libraries such as Hugging Face Transformers, spaCy, NLTK, and Gensim.
• ML and DL Frameworks: Proficiency in ML and Deep Learning frameworks such as TensorFlow, PyTorch, and Scikit-learn.
• Traditional ML Techniques: Familiarity with traditional ML models like SVMs, Logistic Regression, Decision Trees, and KNN etc
• Evaluation Metrics: Familiarity with NLP and ML evaluation metrics (e.g., BLEU, ROUGE, F1, accuracy, precision, recall) and experience designing experiments and tests.
• Communication Skills: Excellent verbal and written communication skills, capable of explaining technical concepts to both technical and non-technical stakeholders.
• Enthusiasm for Learning: A self-motivated individual with a passion for continuous learning and openness to exploring new technologies.
• Teamwork and Collaboration: A team player with the ability to work effectively in a collaborative, fast-paced environment.