Data ScientistAI EngineerAI-ML Ops

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

Conduct exploratory data analysis (EDA) statistical analysis and hypothesis testing to uncover insights and validate business assumptions.

Build scalable end-to-end ML solutions from data preparation and feature engineering to model deployment and monitoring.

Develop and optimize NLP solutions using Transformer-based architectures (BERT GPT Hugging Face etc.) and representation learning techniques.

Design and implement graph-based machine learning solutions using Graph Neural Networks (GNNs) where applicable.

Build automate and maintain MLOps pipelines for model training validation deployment monitoring and lifecycle management.

Deploy and manage machine learning models in cloud environments preferably on Azure using Azure Kubernetes Service (AKS) and CI/CD best practices.

Develop robust data pipelines ETL processes and data integration workflows using modern data engineering practices.

Work with large-scale high-dimensional datasets and optimize model performance scalability and reliability.

Collaborate with cross-functional teams business stakeholders and global partners to translate business requirements into scalable AI and analytics solutions.

Perform model monitoring drift detection observability and continuous improvement for production ML systems.

Mentor junior team members and promote best practices in machine learning MLOps experimentation and software engineering.

Stay updated with advancements in AI Machine Learning Generative AI MLOps and healthcare analytics.