The Junior AI Engineer is a key role within the AI Team, supporting the development of cutting-edge business solutions and contributing to the company's AI initiatives.
Possessing a passion for AI and a desire to learn, the Junior AI Engineer will assist in transforming data into insights that can drive product improvements, enhance customer acquisition, improve retention rates, and boost operating efficiencies. This includes supporting the development and implementation of AI solutions.
The primary goals of the team include:
Supporting the technical design and architecture of AI solutions, and assisting in building and running an AI platform to deliver business-specific Use Case solutions.
Collaborating with the Data Platform team in ingesting and transforming data from multiple systems, modeling data, and engineering data marts to create reusable data assets, including developing and implementing machine learning models and generative AI.
Supporting the development of a company-wide agentic AI solution platform to help scale up AI capabilities across all functions and regions.
Assisting in building AI models and a data science platform that enables Rentokil to derive significant value from AI, from machine learning to gen AI and beyond, and ensuring the quality and reliability of AI solutions deployed on the platform.
Main Tasks:
AI Data Assistance: Assist in the design, building, operation, and deployment of data pipelines using AI techniques and best practices. Support Rentokil's AI efforts by applying data warehousing, data science, and data engineering technologies. Aim for automation to enable a faster time-to-market and better reusability of new AI initiatives.
Collaboration: Work with the AI Product Owner and other team members to create, curate, and maintain high-quality AI assets. Ensure alignment of data architecture and data models across different products and platforms.
Hands-on Involvement: Engage in data engineering tasks as required to support the team and the projects. Support external data collection efforts to aid in the construction of AI models.
Support the development, fine-tuning, and optimising of large language models (LLMs) for corporate use cases, such as querying structured data or automating analytics workflows.
Assist in designing and refining prompts to improve LLM performance in structured data querying and other business-specific applications.
Assist in integrating LLMs with structured data systems (e.g., SQL databases, BigQuery, GCS) to enable natural language querying and advanced analytics.
Support the implementation of MLOps/LLMOps pipelines for deploying LLMs, monitoring their performance, and ensuring scalability.
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