Solution Design:• Collaborate with cross-functional teams to understand business requirements and identify opportunities where AI/ML technologies can be applied effectively.• Design AI-driven solutions that address specific problems or improve existing processes.• Implement solutions to improve customer experience by AI-powered chatbots and virtual assistants.• Design and implement solutions for fraud detection and prevention.• Develop solutions for automating manual processes, providing predictive analytics, and better managing risks associated with financial transactions.Algorithm Development:Develop and implement machine learning algorithms and models tailored to specific tasks, such as classification, regression, clustering, recommendation, and natural language processing. This involves data preprocessing, feature engineering, model selection, and hyperparameter tuning.Data Collection and Analysis:• Identify relevant data sources, collect and preprocess data, and perform exploratory data analysis to gain insights and ensure data quality.• Prepare and transform data for training and evaluation of machine learning models.Model Training and Validation:• Train and validate machine learning models using various techniques, such as supervised, unsupervised, and reinforcement learning.• Employ cross-validation and other methods to assess model performance and generalization.Integration and Deployment:• Integrate trained models into production systems, applications, or platforms. Optimize models for deployment, ensuring efficiency and scalability.• Collaborate with software engineers to ensure seamless integration.Continuous Learning and Improvement:• Stay up-to-date with the latest advancements in AI/ML technologies, tools, and techniques.• Continuously improve existing models and solutions based on feedback, new data, and changing requirements.
Ethical and Responsible AI:
• Address ethical considerations, bias, fairness, and transparency when developing AI systems.• Implement measures to mitigate potential biases and ensure responsible AI practices.Collaboration:• Work closely with data scientists, software developers, data engineers, and domain experts to understand business goals and translate them into technical requirements.• Effective communication and collaboration are crucial for successful AI projects.Performance Optimization:Identify bottlenecks and optimize algorithms and models for speed and efficiency, especially for real-time and resource-constrained environments.Documentation:Maintain detailed documentation of the AI/ML development process, including data preprocessing steps, model architecture, training methodology, and deployment instructions.Qualifications and Skills:• Bachelor's, Master's in Computer Science, Engineering or Mathematics.• Experience in the Financial industry will be a huge plus.• Strong foundation in machine learning algorithms, statistics, and data structures.• Proficiency in programming languages such as Python, and familiarity with libraries like TensorFlow, PyTorch, scikit-learn, and Keras.• Experience with data preprocessing, feature engineering, and model evaluation techniques.• Knowledge of cloud platforms and technologies for scalable AI model deployment.• Problem-solving skills and the ability to adapt algorithms to diverse use cases.• Understanding of software development practices, version control, and agile methodologies.• Excellent communication skills to collaborate with cross-functional teams and convey complex technical concepts to non-technical stakeholders.