Machine Learning and NLP Delivery Lead

IT Delivery

• Primary skills:Technology->Big Data->Natural language processing(NLP),Technology->Machine Learning->Azure ML,Technology->Machine Learning->Python,Technology->OpenSystem->Python - OpenSystem • Knowledge of more than one technology • Basics of Architecture and Design fundamentals • Knowledge of Testing tools • Knowledge of agile methodologies • Understanding of Project life cycle activities on development and maintenance projects • Understanding of one or more Estimation methodologies, Knowledge of Quality processes • Basics of business domain to understand the business requirements • Analytical abilities, Strong Technical Skills, Good communication skills • Good understanding of the technology and domain • Ability to demonstrate a sound understanding of software quality assurance principles, SOLID design principles and modelling methods • Awareness of latest technologies and trends • Excellent problem solving, analytical and debugging skills Key Responsibilities: • Lead the design, development, and deployment of end-to-end machine learning solutions using Python. • Translate business requirements into well-defined ML/NLP problems, solution approaches, and technical roadmaps. • Build, train, evaluate, and optimize ML models, ensuring robustness, scalability, and performance in production environments. • Architect and implement data preprocessing, feature engineering, and model evaluation pipelines. • Guide the implementation of NLP-based solutions (e.g., text classification, entity extraction, sentiment analysis) where applicable. • Review code, establish coding standards, and promote best practices in Python and ML development across the team. • Collaborate with cross-functional stakeholders (product, data, engineering) to align technical solutions with business goals. • Mentor and support junior team members, providing technical guidance and fostering a culture of continuous learning. • Monitor models in production, analyze performance drift, and drive continuous improvement and retraining strategies. • Document solution designs, experiments, and decisions to ensure transparency and maintainability.