Data Science & AI Engineer

What you'll do

• Develop and maintain data pipelines for ingesting data from APIs, databases, and flat files
• Implement ETL/ELT processes using Python and SQL
• Perform data cleaning, transformation, and validation
• Support integration of data across internal and external systems
• Assist in maintaining data workflows and troubleshooting data issues
• Support development of reports, dashboards, and analytical datasets
• Perform exploratory data analysis to generate insights
• Prepare datasets for analytics and AI use cases
• Collaborate with stakeholders to understand reporting and data needs
• Support development of AI-powered application features
• Integrate with pre-built LLM APIs and AI services
• Assist in prompt design and experimentation
• Contribute to basic retrieval and knowledge-based systems
• Test and evaluate AI outputs for accuracy and performance
• Assist in integrating data and AI components into APIs and backend systems
• Support API testing, validation, and debugging (e.g., Postman)
• Collaborate with frontend and backend teams to enable data-driven features
• Write and optimize SQL queries for data extraction and transformation
• Support database schema updates and maintenance
• Ensure data consistency and basic performance optimization
• Assist in deploying and managing data and AI workloads on Azure
• Support configuration of cloud resources under guidance
• Monitor system performance and assist in troubleshooting issues
• Follow secure coding practices for data and AI solutions
• Assist in implementing authentication and access controls
• Ensure adherence to data governance and compliance standards
• Assist in building and maintaining Retool applications
• Support development of internal tools and dashboards
• Integrate applications with APIs and data sources
• Work closely with the Lead Full Stack Engineer (L40) on execution and delivery
• Collaborate with cross-functional teams across engineering, data, and product
• Participate in code reviews, design discussions, and knowledge-sharing
• Continuously learn and adopt best practices in AI, data engineering, and software development

Requirements

• Strong foundation in Python and SQL for data processing
• Basic understanding of APIs, backend systems, and application integration
• Familiarity with AI/ML concepts and LLM-based tools
• Exposure to cloud platforms (Azure preferred)
• Strong analytical thinking and problem-solving ability
• Ability to work effectively in a guided, team-oriented environment
• Experience with vector databases (e.g., Azure AI Search or similar) - preferred
• Exposure to cloud-native backend systems and APIs - preferred
• Basic understanding of MLOps, CI/CD, and model lifecycle management - preferred
• Knowledge of data engineering concepts (CDC, incremental loads, streaming pipelines) - preferred
• Familiarity with monitoring, observability, and performance tuning - preferred
• Understanding of Responsible AI practices and governance - preferred
• Experience with analytics tools (e.g., Power BI, Tableau) - preferred

Tech stack