Senior Data Scientist-Machine Learning Engineer

Role & responsibilities

• Expertise in ML/DL, model lifecycle management, and MLOps (MLflow, Kubeflow).
• Proficiency in Python, TensorFlow, PyTorch, Scikit-learn, and Hugging Face models.
• Strong experience in NLP, fine-tuning transformer models, and dataset preparation.
• Hands-on with cloud platforms (AWS, GCP, Azure) and scalable ML deployment (Sagemaker, Vertex AI).
• Experience in containerization (Docker, Kubernetes) and CI/CD pipeline.
• Knowledge of distributed computing (Spark, Ray), vector databases (FAISS, Milvus), and model optimization (quantization, pruning).
• Familiarity with model evaluation, hyperparameter tuning, and model monitoring for drift detection.

Preferred candidate profile

• Design and implement end-to-end ML pipelines from data ingestion to production.
• Develop, fine-tune, and optimize ML models, ensuring high performance and scalability.
• Compare and evaluate models using key metrics (F1-score, AUC-ROC, BLEU etc.).
• Automate model retraining, monitoring, and drift detection.
• Collaborate with engineering teams for seamless ML integration.
• Mentor junior team members and enforce best practices.