Machine Learning Specialist

. ML Fundamentals: In-depth understanding of statistical modelling, machine learning algorithms (supervised, unsupervised, deep learning), model evaluation metrics, and techniques to address issues like overfitting.

• Data Handling: Experience with data preprocessing, feature engineering, and managing structured and unstructured data for ML tasks. Knowledge of AI Frameworks like LangChain, LangGraph, and LangSmith

• Programming Proficiency: advanced-level proficiency in Python and experience with core ML/Data Science libraries (e.g., PyTorch, Pandas, NumPy).

• Experience implementing MLOps principles, including CI/CD pipelines (GitHub Actions, CodePipeline) and Infrastructure as Code (IaC) tools (Terraform or AWS CDK).