Machine Learning Engineer (Multimodal)

Machine 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.