AI Engineer

The AI Engineer position will be responsible to shape, create and integrate AI solutions with particular focus on Natural Language Processing, Large Language Models and Multi-Modal Generative AI. The candidate will be working alongside other members of the CoE, Divisional Colleagues and Data Engineering teams to further drive AI development and innovation. The AI Engineer will work closely with data scientists, delivery leads, product managers, content matter experts, and technology teams to develop, automate and scale-up advanced AI solutions to address key customer problems whilst also helping to develop methodologies and reusable solutions.

As the CoE AI Engineer, you will be responsible for providing technical expertise and leadership across all aspects of AI engineering process, from data acquisition, tagging, embedding, protection and storage.

Your Role:

• Prepare and preprocess data to ensure it is ready for use by machine learning models and AI solutions. Work with a variety of data types including PDFs, Word documents, Excel files, HTML, audio, video, and text, as well as various databases.
• Build LLM applications including RAG applications, fine tuning LLMs and embedding models, agent/reasoning applications.
• Deploy LLM applications to cloud infrastructure (on AWS is plus)
• Manage production-grade LLM application involving RAG evaluation and improvements (RAGAS, advanced retrieval techniques), monitoring and visibility tooling and scaling LLM applications (Amazon SageMaker)
• Stay Current with Emerging Technologies: Keep up-to-date with the latest developments in data engineering, machine learning, and AI technologies to continually enhance data capabilities.

• Strong understanding and hands-on experience in various ML techniques and algorithms, including RAG, fine-tuning models and working with large language models.
• Mastery in Langchain and Python programming languages, essential for AI development and data manipulation tasks.
• Proficiency in handling and processing large datasets, ensuring data quality and accessibility, including expertise in vectorized datasets. Experience with Data Integration Tools: Proficiency in ETL/ELT tools and practices.
• Competency in deploying and managing AI solutions on AWS cloud infrastructure. Experience with containerization technologies like Docker.
• Understanding and application of MLOps practices for efficient model lifecycle management.
• Knowledge of data security and privacy practices.
• Effective problem-solving skills to address challenges encountered in AI development projects.
• Strong collaboration and communication skills for effective teamwork and conveying technical concepts clearly to stakeholders.
• Knowledge and experience in MarTech preferred, applied in AI development projects.
• Willingness to continuously learn and adapt to new technologies and methodologies in AI and data engineering.