Job Description:

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

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