Senior Data Scientist - NLP/Deep Learning

Key Responsibilities :

- Design, develop, and deploy advanced machine learning and deep learning models to solve complex business problems.

- Lead projects involving natural language processing (NLP), including fine-tuning transformer-based models for tasks such as classification, summarization, and generation.

- Manage the entire ML lifecycle including data preparation, model training, evaluation, optimization, deployment, and monitoring.

- Implement MLOps practices using tools like MLflow or Kubeflow to enable reproducibility, scalability, and automation of ML pipelines.

- Collaborate with cross-functional teams including data engineers, product managers, and software developers to productionize models.

- Utilize cloud platforms (AWS, GCP, Azure) and ML services (e.g., SageMaker, Vertex AI) for scalable model training and deployment.

- Apply best practices for model optimization techniques such as quantization and pruning to enhance performance.

- Integrate ML solutions into containerized environments using Docker, Kubernetes, and CI/CD pipelines.

- Work with distributed computing frameworks (e.g., Apache Spark, Ray) for handling large datasets efficiently.

- Explore and implement vector databases like FAISS or Milvus for similarity search and retrieval-based AI systems.

- Continuously monitor model performance, implement drift detection, and conduct hyperparameter tuning for model stability and accuracy.

- Stay up to date with the latest advancements in AI/ML and proactively introduce improvements into the data science stack.

Required Skill Set :

- Strong expertise in Machine Learning, Deep Learning, and MLOps

Proficiency in :

- Python, TensorFlow, PyTorch, Scikit-learn

- Hugging Face and other NLP model libraries

In-depth knowledge and hands-on experience in :

- NLP and fine-tuning transformer models

- Model lifecycle management and ML system design

- Cloud platforms : AWS (SageMaker), GCP (Vertex AI), Azure ML

- Containerization using Docker and Kubernetes

- CI/CD pipelines for ML workflows

- Distributed computing frameworks : Spark, Ray

- Vector databases : FAISS, Milvus

- Model optimization : quantization, pruning

- Model evaluation, hyperparameter tuning, and drift detection

Preferred Qualifications :

- Published work in machine learning/NLP or contributions to open-source projects.

- Experience mentoring junior data scientists or leading project teams.