Job DescriptionMachine Learning Engineer (Multimodal)
Up to £100,000
London Hybrid/Remote
Driving innovation with AI and machine learning to revolutionize financial services and enhance customer experiences.
COMPANY
Harnham has partnered with a leading Fintech company using advanced AI technology to transform financial services. Their cutting-edge approach has led to the development of innovative financial solutions, making significant strides in areas such as fraud detection, personalized financial advice, and risk management.
ROLE:
- Lead the development of AI algorithms, focusing on AI/ML techniques and Large Language Models (LLMs) to drive innovation in financial services.
- Build and test machine learning models, advocate for best coding practices, and ensure high-quality results through thorough testing.
- Collaborate closely with data scientists, financial analysts, and engineers to develop and implement AI/ML tools for data analysis.
- Leverage expertise in multimodal LLMs, especially in search and retrieval-augmented generation (RAG) technologies, to enhance model performance and application in financial contexts.
YOUR SKILLS AND EXPERIENCE:
- MSc or PhD in a STEM subject.
- Proven experience with the implementation of Machine Learning models and Large Language Models, including multimodal LLMs.
- MLOps/DevOps experience with CI/CD pipelines.
- Proficiency in TensorFlow, Kubernetes, MLFlow, Kafka, and Airflow.
- Strong Python skills are essential; experience with AWS and Spark is beneficial.
- Excellent communication skills and experience engaging with team members and stakeholders.
- Expertise in large-scale computation and experience in a research or tech-driven environment.
- Familiarity with LLMs and tools like Langchain, with specific exposure to search and retrieval-augmented generation (RAG) technologies.
- Keen interest in financial technology and the Fintech space.
BENEFITS:
- Salary up to £100,000
- Bonus
- Healthcare & Pension
HOW TO APPLY:
Please register your interest by sending your CV to Luc Simpson-Kent via the link on this page.