Senior Data Scientist | Machine Learning Engineer (MLE)

You should have expertise in ML/DL, model lifecycle management, and MLOps tools such as MLflow and Kubeflow. Proficiency in Python, TensorFlow, PyTorch, Scikit-learn, and Hugging Face models is essential. You must possess strong experience in NLP, fine-tuning transformer models, and dataset preparation. Hands-on experience with cloud platforms like AWS, GCP, Azure, and scalable ML deployment tools like Sagemaker and Vertex AI is required. Knowledge of containerization using Docker and Kubernetes, as well as CI/CD pipelines, is expected. Familiarity with distributed computing tools like Spark and Ray, vector databases such as FAISS and Milvus, and model optimization techniques like quantization and pruning is necessary. Additionally, you should have experience in model evaluation, hyperparameter tuning, and model monitoring for drift detection. As a part of your roles and responsibilities, you will be required to design and implement end-to-end ML pipelines from data ingestion to production. Developing, fine-tuning, and optimizing ML models to ensure high performance and scalability is a key aspect of the role. You will be expected to compare and evaluate models using key metrics like F1-score, AUC-ROC, and BLEU. Automation of model retraining, monitoring, and drift detection will be part of your responsibilities. Collaborating with engineering teams for seamless ML integration, mentoring junior team members, and enforcing best practices are also important aspects of the role. This is a full-time position with a day shift schedule from Monday to Friday. The total experience required for this role is 4 years, with at least 3 years of experience in Data Science roles. The work location is in person. Application Question: How soon can you join us ,