AI/ML Engineer

We are seeking a Senior AI/ML Engineer with expertise in Generative AI, traditional AI approaches, and MLOps to join our innovation team. The ideal candidate will focus on identifying and de-risking key assumptions rather than primarily delivering production-grade pipelines. However, a demonstrable understanding of production-grade systems is essential. You will play a crucial role in pipeline optimization and AI development, ensuring smooth integration and deployment within our innovative projects.

Key Responsibilities:
-Conduct AI research to explore new techniques and approaches for Generative AI and traditional AI.
-Implement and test AI/ML pipelines in a research context to evaluate and de-risk concepts from a technical perspective.
-Leverage Generative AI techniques, including prompt engineering and evaluation, experimenting with open-source models to validate their effectiveness for specific applications.
-Design prototype applications that align with the overall architectural strategy and company objectives, focusing on innovative solutions.
-Manage and prioritise workload effectively, providing accurate task estimates and meeting deadlines.
-Continuously seek feedback to improve research processes and recognise opportunities for the team and wider business.
-Conduct engineering spikes and feasibility experiments to de-risk technical concepts and ensure innovative solutions.

Please say “Hi Redbrain ML” to introduce your application so we know you’ve read the job offer.

Essential Skills and Experience:
-Proficiency in BigQuery/Postgres.
-Experience with Kubernetes and Docker, including leveraging GPU resources on a cluster.
-Strong Python skills, particularly with Pandas, Numpy, Transformers, FastAPI, Jupyter notebooks, vLLM, and DeepSpeed.
-Experience with Golang is highly beneficial.
-Expertise in Generative AI, including prompt engineering and model evaluation.
-Fundamental understanding of ETL pipelines and large-scale data processing.
-Familiarity with version control systems and writing/maintaining automated tests (unit testing, integration testing).
-Passion for technology and development best practices.
-Experience in AI research and implementing engineering spikes to de-risk concepts.
-Ability to design and prototype AI applications aligned with architectural strategies.
-Effective workload management and task prioritisation skills.
-Continuous improvement mindset with the ability to seek and act on feedback.