AI/ML Development: 1. Model Development and Training: - Design, develop, and train machine learning models and algorithms to solve specific business problems. - Implement various machine learning techniques including supervised, unsupervised, and reinforcement learning. 2. Data Preprocessing and Analysis: - Perform data cleaning, transformation, and preprocessing to prepare data for model training. - Analyze and interpret complex data sets to identify patterns and trends. - NLP, nltk and NER are preferred. 3. Model Evaluation and Optimization: - Evaluate model performance using appropriate metrics and fine-tune models to improve accuracy and efficiency. - Implement model optimization techniques such as hyperparameter tuning and feature engineering. 4. Deployment and Maintenance: - Deploy machine learning models into production environments ensuring scalability and reliability. - Monitor and maintain models to ensure they continue to perform well over time. Python Development: Software Development: - Write clean, maintainable, and efficient Python code. - Develop and maintain Python-based applications, APIs(Flask and Fast API preferred) , and microservices. Code Review and Mentorship: - Conduct code reviews to ensure adherence to best practices and coding standards. - Mentor junior developers and provide guidance on best practices and development techniques. Integration and Testing: - Integrate machine learning models with existing systems and applications. - Develop and execute unit tests, integration tests, and end-to-end tests to ensure software quality. Automation and Scripting: - Automate repetitive tasks and workflows using Python scripts. - Develop and maintain automation tools and frameworks. Collaboration and Communication: - Collaborate with data scientists, data engineers, product managers, and other stakeholders to understand requirements and deliver solutions. - Participate in design and architecture discussions to shape the direction of projects. Documentation and Reporting: - Document code, processes, and methodologies to ensure knowledge sharing and maintainability. - Communicate findings, progress, and results to stakeholders through reports and presentations. Continuous Learning and Improvement: 1. Stay Updated with Industry Trends: - Keep up-to-date with the latest developments in AI/ML and Python technologies. - Experiment with new tools, libraries, and frameworks to improve existing solutions and processes. Problem Solving and Innovation: 1. Innovative Solutions: - Identify opportunities to apply AI/ML techniques to solve new and existing problems. - Propose and implement innovative solutions to improve business processes and outcomes. 2. Technical Challenges: - Tackle complex technical challenges and provide effective solutions. - Troubleshoot and resolve issues related to machine learning models and Python applications. Mandatory Skills: Strong proficiency in Python and its libraries (e.g., NumPy, Pandas, Scikit-Learn, TensorFlow, PyTorch). NLP, nltk and NER hand on knowledge is required. Experience with data preprocessing, feature engineering, and model evaluation. Knowledge of software development principles, design patterns, and best practices. Excellent problem-solving and analytical skills. Strong communication and collaboration abilities. Ability to work independently and as part of a team. Continuous learning mindset and adaptability to new technologies.
