Applied AI ML Associate

J.P. Morgan; Glasgow; On-site; GBP 60,000 - 80,000; Full time

Job summary

A leading financial services firm in Glasgow is seeking a Senior Applied AI/ML Associate to innovate and drive automation within the private banking sector. The role involves developing AI solutions using Python, collaborating with stakeholders, and leveraging machine learning techniques to enhance decision-making processes. Candidates should have an advanced degree in a technical field and strong experience in NLP and Python. This position offers the opportunity to work in a dynamic team environment focused on pushing the boundaries of AI in finance.

Qualifications

• Advanced degree in a quantitative or technical discipline or significant practical experience.
• Experience with NLP, LLM, and ML techniques for solving business problems.
• Ability to communicate complex concepts to technical and business audiences.

Responsibilities

• Develop GenAI and Agentic AI solutions using Python.
• Collaborate to address business needs with NLP/ML solutions.
• Monitor and improve model performance through active learning.

Skills

NLP

Machine Learning

Python

Statistical Analysis

Deep Learning

Education

Advanced degree (MS or PhD) 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.

Job 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.