JPMorgan AI/ML Software Engineer III 

United Kingdom, Scotland 

912878847

Job Responsibilities

• Design and architect end to end solutions in AI domain, from Pattern matching, Chatbot implementation, and using GenAI.
• Proactively develop an understanding of key business problems and processes.
• Execute tasks throughout the model development process, including data wrangling/analysis, model training, testing, and selection.
• Generate structured and meaningful insights from data analysis and modelling exercises and present them in an appropriate format according to the audience.
• Collaborate with other data scientists and machine learning engineers to deploy machine learning solutions.
• Conduct ad-hoc and periodic analysis as required by business stakeholders, the model risk function, and other groups.

Required qualifications, capabilities, and skills

• Formal training or certification on AI/ML concepts and proficient applied experience.
• Proven experience post-advanced degree (MS, PhD) in a quantitative field (e.g., Data Science, Computer Science, Applied Mathematics, Statistics, Econometrics).
• Experience in statistical inference and experimental design (such as probability, linear algebra, calculus).
• Data wrangling: understanding complex datasets, cleaning, reshaping, and joining messy datasets using Python.
• Practical expertise and work experience with ML projects, both supervised and unsupervised.
• Proficient programming skills with Python, including libraries such as NumPy, pandas, and scikit-learn, as well as R.
• Understanding and usage of the OpenAI API.
• NLP: tokenization, embeddings, sentiment analysis, basic transformers for text-heavy datasets.
• Experience with LLM & Prompt Engineering, including tools like LangChain, LangGraph, and Retrieval-Augmented Generation (RAG).
• Experience in anomaly detection techniques, algorithms, and applications.
• Excellent problem-solving, communication (verbal and written), and teamwork skills.

Preferred qualifications, capabilities, and skills

• Experience with big data frameworks, with a preference for Databricks.
• Experience with databases, including SQL (Oracle, Aurora), and Vector DB.
• Familiarity with version control systems such as Bitbucket and GitHub.
• Experience with graph analytics and neural networks.
• Experience working with engineering teams to operationalize machine learning models.