Junior Mid-Level Machine Learning Data Engineer

Assist to develop and maintain scalable and efficient data pipelines using technologies such as Spark, Python, and relevant ETL tools to support our machine learning models, including those leveraging LLMs, and analytical n eeds.

Support with the architect and implement robust data warehousing solutions and data models that ensure data quality, integrity, and performance, catering to the specific data requirements of advanced AI models.

Assist in the development, testing, and deployment of machine learning models, including exploration and integration of Large Language Models (LLMs) and other novel AI architectures, collaborating closely with Data Scientists to productionize innovative s olutions.

Help transform engineer approaches for storing, transforming, transporting, synchronising, archiving, and securing large and complex datasets, including unstructured and semi-structured data crucial for training and deploying advanced AI models.

Participate in the evaluation and testing of new machine learning models and frameworks, including LLMs, to assess their potential and applicability to our products.

Assist to identify and resolve performance bottlenecks, data quality issues, and other pain points within our data and ML infrastructure. Proactively recommend and implement solutions for optimisation and improvement, especially in the context of deploying large-scale AI models.

Define and govern data modelling and design standards, best practices, and development methodologies within the team, considering the unique challenges and opportunities presented by LLMs and other advanced AI.

Create and maintain comprehensive technical documentation for data pipelines, data models, and machine learning workflows, including details specific to LLM integration and testing.

Collaborate effectively with Business Analysts, Data Scientists, and other engineering teams to understand data requirements and deliver impactful data and AI solutions.

Stay abreast of the latest advancements in data engineering, machine learning (including LLMs and generative AI), and big data technologies, and actively participate in the evaluation and integration of promising new t echnologies.