Responsibilities
- Help build and sustain high-performance data pipelines using tools like Spark, Python, and ETL frameworks to support machine learning and analytics, including systems using large language models.
- Support the design and implementation of reliable data warehouse architectures and models that ensure accuracy, consistency, and speed for AI-driven applications.
- Collaborate in creating, evaluating, and deploying machine learning models, including experimentation with large language models and emerging AI frameworks, alongside data science teams.
- Contribute to engineering strategies for managing complex data, including storage, transformation, transfer, synchronization, archiving, and security, particularly for unstructured data used in AI training.
- Take part in assessing new machine learning models and technologies, including large language models, to determine their fit for product development.
- Help detect and fix performance issues, data inaccuracies, and system inefficiencies in data and machine learning platforms, with a focus on optimizing large-scale AI deployments.
- Support the establishment of data modeling standards, design principles, and development practices tailored to the needs of advanced AI systems.
- Produce and update detailed technical documentation for data workflows, models, and machine learning processes, including specifics on large language model integration.
- Work closely with business analysts, data scientists, and engineering teams to interpret data needs and deliver effective data and AI solutions.
- Keep current with developments in data engineering, machine learning, generative AI, and big data technologies, and help assess and adopt new tools and methods.
Work Arrangement
Remote — London
Other
- AI tools may be used during hiring to assist with resume review, application analysis, and detecting inconsistencies. These tools support but do not replace human decision-making.
- For details on how your personal data is handled, please contact us directly.
