Skill Sets :

- 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 pipelines

- 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

Roles & Responsibilities :

- 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