Job Description

The AI-ML Architect is responsible for leading the design, development, and implementation of artificial intelligence (AI) and machine learning (ML) solutions across the enterprise

Should lead the end-to-end architecture and development of Artificial Intelligence and Machine learning solutions.

Responsible for understanding and implementing Deep Learning models for AI-ML solutions, meeting development schedules and ensuring the delivered solution meets the technical specifications

Should Plan, architect, design, develop, test and maintain key software enhancements, especially related to machine learning capabilities.

Responsible for architecting the AI-ML models and how they are used in a production environment.

Drive architecture and design decisions for the product and platform focusing on the machine learning flow.

Implement test cases to automate testing of frontend and backend code.

Industry experience in predictive modeling, data science, and experience analyzing terabyte-size datasets.

Previous experience in a ML or data scientist role and a track record of building ML or DL models and experience.

Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations.

Consulting experience and track record of helping customers with their AI needs.

Experience with imperative programming languages such as Java or C/C++.

Experience working with one or more Machine Learning and/or Deep Learning frameworks such as Spark ML, TensorFlow, PyTorch, Caffe, Scikit-learn, etc.,

Experience in implementing and deploying AI\ Machine Learning solutions (using various models, such as CNN, RNN, Fuzzy logic, Q learning, SVM, Ensemble, Logistic Regression, Random Forest etc.).

Research, analyze, recommend and select technical approaches to address challenging development and data integration problems related to ML Model training and deployment in Enterprise Applications.

Experience developing best practices and recommendations around tools/technologies for ML life-cycle capabilities such as Data collection, Data preparation, Feature Engineering, Model Management, MLOps, Model Deployment approaches and Model monitoring and tuning.

Advanced AI ML techniques: Random Forest, Boosting Algorithm, Decision Trees, Neural Networks, Deep Learning, Support Vector Machines, Clustering, Bayesian Networks, Reinforcement Learning, Feature Reduction / engineering, Anomaly deduction, Natural Language Processing (incl. sentiment analysis, Topic Modeling), Natural Language Generation