Data Science & AI Engineer
(Azure, GenAI, Data & Decisioning Systems)
Role Summary
We are seeking a motivated and hands-on Data Science & AI Engineer (L35) to support the development and implementation of data-driven and AI-powered solutions. This role works under the guidance of the Lead Full Stack Engineer (L40) and focuses on execution, experimentation, and delivery of data pipelines, analytics, and AI-enabled features.
The ideal candidate will contribute to building, testing, and integrating data and AI components, while gaining exposure to enterprise systems and evolving toward broader technical responsibilities over time.
Core Responsibilities
Data Engineering & Processing
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
Data Analysis & Reporting
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
AI / ML Implementation
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
Application & API Support
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
Database Development
Write and optimize SQL queries for data extraction and transformation
Support database schema updates and maintenance
Ensure data consistency and basic performance optimization
Cloud & Platform Support (Azure)
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
Security & Compliance Support
Follow secure coding practices for data and AI solutions
Assist in implementing authentication and access controls
Ensure adherence to data governance and compliance standards
Internal Tools & Platform Support (Retool)
Assist in building and maintaining Retool applications
Support development of internal tools and dashboards
Integrate applications with APIs and data sources
Collaboration & Development
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
Profile Summary
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
Preferred / Good to Have
Experience with vector databases (e.g., Azure AI Search or similar)
Exposure to cloud-native backend systems and APIs
Basic understanding of MLOps, CI/CD, and model lifecycle management
Knowledge of data engineering concepts (CDC, incremental loads, streaming pipelines)
Familiarity with monitoring, observability, and performance tuning
Understanding of Responsible AI practices and governance
Experience with analytics tools (e.g., Power BI, Tableau)
Preferred Media Domain Experience
Programmatic advertising systems (DSP, SSP)
Personalization and recommendation platforms
Identity resolution and post-cookie ecosystem
Marketing analytics, attribution, and campaign optimization systems
Ad-tech ecosystem – programmatic advertising (DSP/SSP), targeting, identity resolution, and campaign performance measurement