Role Requirements: • 8+ years of experience in Machine learning engineering /MLE , data science and data engineering, with a proven track record of delivering impactful solutions. • Proven experience in AI Application & Infrastructure Optimization in terms of Capacity, Performance and Cost • Architectural Optimization -Experience in optimization of solution architectures with a focus on data security, privacy, application reliability, infrastructure scalability, and cost-efficiency for Data Science and Generative AI platforms. • Scalable AI Solution Deployment - Experience in designing and implementing scalable AI and Data Science solutions capable of handling large-scale, distributed data and compute workloads across hybrid and cloud-native environments(OCI). • Performance Tuning for AI/ML and GenAI Workloads - Experience in hyperparameter tuning and performance optimization for AI/ML and Generative AI models to maximize accuracy, efficiency, and resource utilization. • AI Lifecycle Management - Experience in robust AI pipelines for model versioning, testing, validation, and deployment, ensuring seamless integration into production environments with CI/CD best practices. • AI Full stack : Experience in in any full stack /development experience in Java, or .Net or React, Node.js, Flask/Django, REST APIs. • Proficiency in Python, SQL, and machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch. • Experience in EDA, data engineering, creating data pipelines and processing huge volume of data. • Understanding of cloud platforms (OCI, Azure) and containerization (Docker, Kubernetes). • Excellent problem-solving, communication, and stakeholder management skills. • Prior experience in a customer-facing or product-oriented role. • Master’s in computer science, Machine Learning , Data Science, Statistics, or a related field.'