Job
Description
Key Responsibilities: AI-Driven Development: Design and develop an intelligent data conversion application using AI technologies. Data Mapping & Validation: Implement AI algorithms to accurately map source-to-target data and validate integrity. Automated Code Generation: Develop AI models to automate code creation for transformation scripts. Code Conversion: Use AI to facilitate smooth and efficient legacy-to-modern code conversion. Monitoring & Troubleshooting: Ensure the health and performance of the AI-powered conversion system. Cross-Functional Collaboration: Work closely with data engineers, developers, AI/ML engineers, and business analysts. Documentation: Maintain detailed records of models, processes, and tools used. Continuous Learning: Stay abreast of the latest trends in AI, ML, data science, and cloud technologies. What Youll Need: Bachelor's or Master’s in Data Science , Computer Science , Artificial Intelligence , or a related field. Strong experience in Python , SQL , and other relevant programming languages. Hands-on with AI/ML frameworks like TensorFlow, PyTorch, etc. Proven experience with AI-driven data mapping , code generation , and validation techniques . Knowledge of code conversion methodologies and tools. Experience working with databases such as SQL Server and MongoDB . Strong analytical and problem-solving abilities. Excellent collaboration and communication skills. High attention to detail and data accuracy. Comfortable with a "fail fast, learn faster" approach in agile, fast-paced environments. 5+ years of relevant industry experience. Preferred Qualifications: Experience in the Financial Services domain with awareness of compliance requirements. Certifications in Data Science or AI/ML. Familiarity with data wrangling , ETL processes , and Master Data Management . Exposure to DevOps tools like Jira, Bitbucket, and Confluence. Hands-on with Azure technologies : Cognitive Services, Synapse Analytics, Databricks, and Azure ML. Prior experience delivering AI-powered data solutions , such as: Seamless Data Schema Conversion AI-Driven Data Validation for Migration Intelligent Code Generation Historical Data Transformation and Archiving AI-Augmented Reconciliation and Error Detection
