Applied AI ML Associate

J.P. Morgan; Glasgow; On-site; GBP 70,000 - 90,000; Full time

Job summary

A leading financial institution in the UK is seeking a Senior Applied AI/ML Associate to develop and implement advanced AI solutions. This role involves collaborating with stakeholders to create NLP and ML solutions that tackle complex business challenges. Candidates should possess an advanced degree and extensive experience in Python programming and machine learning. Join a dynamic team to revolutionize client service and process efficiency. Competitive salary offered.

Qualifications

• Advanced degree or significant practical experience in industry.
• Experience with NLP, LLM, and ML techniques.
• Good understanding of ML algorithms like clustering and decision trees.

Responsibilities

• Develop and implement GenAI solutions using Python.
• Collaborate to identify business needs and develop NLP/ML solutions.
• Monitor and improve model performance through feedback.

Skills

NLP

Machine Learning

Python programming

Deep learning frameworks

Statistical analysis

Education

Advanced degree in a quantitative or technical discipline

Tools

PyTorch

Transformers

HuggingFace

Job description

Revolutionize AI and machine learning to solve complex problems and promote innovation.

As a Senior Applied AI/ML Associate within our dynamic team of innovators and technologists, you will revolutionize how the Private Bank services and advises clients, deepen client engagements, and promote process transformation. You will analyze existing processes and vast amounts of data to design autonomous AI agents, leveraging advanced data analysis, statistical modeling, and AI/ML techniques to solve complex business challenges through high-quality, cloud-centric software delivery.

Responsibilities

• Develop and implement GenAI and Agentic AI solutions using Python to enhance automation and decision-making processes.
• Collaborate with internal stakeholders to identify business needs and develop NLP/ML solutions that address client needs and drive transformation.
• Apply large language models (LLMs), machine learning (ML) techniques, and statistical analysis to enhance informed decision-making and improve workflow efficiency, which can be utilized across investment functions, client services, and operational process.
• Collect and curate datasets for model training and evaluation.
• Perform experiments using different model architectures and hyperparameters, determine appropriate objective functions and evaluation metrics, and run statistical analysis of results.
• Monitor and improve model performance through feedback and active learning.
• Collaborate with technology teams to deploy and scale the developed models in production.
• Deliver written, visual, and oral presentation of modeling results to business and technical stakeholders.
• Stay up-to-date with the latest research in LLM, ML and data science. Identify and leverage emerging techniques to drive ongoing enhancement.

Required qualifications, capabilities, and skills

• Advanced degree (MS or PhD) in a quantitative or technical discipline or significant practical experience in industry.
• Experience in applying NLP, LLM and ML techniques in solving high-impact business problems, such as semantic search, information extraction, question answering, summarization, personalization, classification or forecasting.
• Advanced python programming skills with experience writing production quality code
• Good understanding of the foundational principles and practical implementations of ML algorithms such as clustering, decision trees, gradient descent etc.
• Hands-on experience with deep learning toolkits such as PyTorch, Transformers, HuggingFace.
• Strong knowledge of language models, prompt engineering, model finetuning, and domain adaptation.
• Familiarity with latest development in deep learning frameworks.
• Ability to communicate complex concepts and results to both technical and business audiences.

Preferred qualifications, capabilities, and skills

• Prior experience of developing solutions for Financial domain
• Exposure to distributed model training, and deployment
• Familiarity with techniques for model explainability and self validation