Senior Machine Learning Engineer/Data Scientist (NLP)

Description

We seek an experienced Senior Machine Learning Engineer/Data Scientist specializing in Natural Language Processing (NLP) and expertise in Generative AI. In this role, you will lead the design, development, and deployment of advanced NLP models and generative AI applications to solve complex business problems. You will be responsible for building and optimizing machine learning pipelines, particularly focusing on deep learning and generative models. This role will involve working on various projects, including text analysis, language modeling, and model deployment in production environments. You will also be instrumental in applying generative models for creative and business purposes, such as text generation and data augmentation.

Primary Responsibilities/Essential Functions

• Natural Language Processing (NLP):
• Lead the design and implementation of advanced NLP models for tasks such as text classification, named entity recognition (NER), topic modeling, sentiment analysis, and language translation.
• Apply cutting-edge deep learning techniques like Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTMs), and transformer models (e.g., BERT, GPT) for complex NLP tasks.
• Leverage pre-trained language models, word embeddings (Word2Vec, GloVe, FastText), and fine-tune them to meet custom business requirements.
• Generative AI:
• Apply Generative AI techniques, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformer-based models (e.g., GPT-3, T5), to develop solutions for text generation, data augmentation, and other creative use cases.
• Explore innovative applications of Generative AI in content creation, including summarization, question generation, and dialogue systems.
• Stay up-to-date with the latest advancements in Generative AI, and integrate them into existing pipelines to enhance model performance and functionality.
• Collaborate with cross-functional teams to explore new business applications for generative models, such as synthetic data generation for model training or content generation for marketing.
• Supervised Learning:
• Develop and optimize machine learning models using supervised learning techniques such as regression, classification, Support Vector Machines (SVMs), and decision trees.
• Evaluate models using performance metrics such as accuracy, precision, recall, F1 score, and use cross-validation to ensure model robustness.
• Unsupervised Learning:
• Use clustering algorithms, such as K-means and hierarchical clustering, and dimensionality reduction techniques like Principal Component Analysis (PCA) to uncover hidden patterns in data.
• Implement anomaly detection models to identify rare events or outliers in datasets, supporting business intelligence and decision-making.
• Deep Learning:
• Lead the design and deployment of deep learning models using Convolutional Neural Networks (CNNs), RNNs, LSTMs, and transformers to handle complex tasks in both NLP and Generative AI.
• Optimize and fine-tune deep learning architectures to improve accuracy, performance, and scalability of models in production.
• Model Deployment and MLOps:
• Deploy machine learning models into production using cloud platforms such as Azure ML, ensuring scalability and performance.
• Implement MLOps best practices, including CI/CD pipelines, model versioning, and automated retraining using tools like MLflow, Kubeflow, and Azure ML Pipelines.
  
  
  

Monitor models post-deployment, track model performance, identify drift, and retrain models as necessary to maintain their relevance and accuracy.

Qualifications

Education :

• Master’s or PhD in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related field.
  
  
  

Experience:

• 5 years of experience in machine learning, with a focus on Natural Language Processing (NLP) and Generative AI.
• Extensive experience with deep learning frameworks such as TensorFlow, Keras, or PyTorch for building and deploying models.
• Proven expertise in deploying models to production environments using cloud platforms such as Azure ML, AWS, or GCP.
• Familiarity with MLOps practices, including CI/CD, model versioning, and automated retraining.
  
  
  

Technical Skills:

• Proficiency in Python and machine learning libraries such as scikit-learn, TensorFlow, PyTorch, Keras, Hugging Face Transformers, NLTK, and SpaCy.
• Expertise in NLP techniques, including tokenization, word embeddings, transformers (e.g., BERT, GPT), and sequence models like RNNs and LSTMs.
• Experience with Generative AI models such as GANs, VAEs, and transformers (e.g., GPT-3, T5) for tasks like text generation and data augmentation.
• Experience with cloud platforms for model deployment, including Azure ML, AWS, or GCP.
• Familiarity with MLOps tools such as MLflow, Kubeflow, and Azure ML Pipelines for tracking, monitoring, and automating ML models.
  
  
  

Leadership and Soft Skills :

• Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.
• Collaborate with data engineers, product managers, and other stakeholders to translate business requirements into machine learning solutions.