AI ML Software Engineer III - Chief Data & Analytics Office

JPMorgan Chase & Co.; London; On-site; GBP 60,000 - 80,000; Full time

Job summary

A leading financial services firm is seeking an AI ML Software Engineer III to join its Chief Data & Analytics Office in London. You will design and build AI systems for modernizing compliance through scalable solutions. The ideal candidate will have a Bachelor's degree in Computer Science and strong Python programming skills. Responsibilities include building ML models and collaborating with cross-functional teams. This position offers an opportunity to work on complex data usage questions and ensure effective AI decision-making.

Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, or related field.
• Commercial software development experience, ideally with exposure to ML/AI systems.
• Strong programming skills in Python; familiarity with web frameworks.

Responsibilities

• Build and integrate ML models into structured backend services.
• Write production-ready Python code for model inference and validation.
• Collaborate with teams to understand requirements and execute implementation.

Skills

Python programming

Machine Learning

AI systems knowledge

Data validation

CI/CD pipelines

Education

Bachelor’s degree in Computer Science

Tools

Flask

FastAPI

Git

Job description

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AI ML Software Engineer III - Chief Data & Analytics Office, London

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Client:

Location:

London, United Kingdom

Job Category:

Other

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EU work permit required:

Yes

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Job Reference:

b5970cb8e58a

Job Views:

11

Posted:

12.08.2025

Expiry Date:

26.09.2025

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Job Description:

Join JPMorgan Chase's Chief Data & Analytics Office (CDAO) and be part of a mission to modernize compliance through scalable and explainable AI. As a Machine Learning Engineer, you'll design and build systems that answer critical data usage questions with prediction, logic, proof, and intelligent automation. Work at the intersection of applied machine learning, AI reasoning systems, and data governance to tackle complex problems and build ML solutions that make decisions.

As a Machine Learning Engineer within JPMorgan Chase's Chief Data & Analytics Office (CDAO), you will design and build systems that answer critical data usage questions with prediction, logic, proof, and intelligent automation. You will work at the intersection of applied machine learning, AI reasoning systems, and data governance to tackle complex problems and build ML solutions that make decisions. You will build and integrate ML models into structured backend services, write production-ready Python code, and assist in building automated workflows. You will collaborate with VP engineers and cross-functional partners to understand requirements and execute implementation. Your role involves participating in code reviews, quality assurance, and ongoing system improvement.

Job Responsibilities:

• Build and integrate ML models into structured backend services (APIs, pipelines, batch processors).
• Write production-ready Python code to support model inference, validation, and logging.
• Assist in building automated workflows for data ingestion, model deployment, and metadata tagging.
• Build dashboards, logs, or simple UI tools to visualize and debug decision outcomes.
• Collaborate with VP engineers and cross-functional partners to understand requirements and execute implementation.
• Participate in code reviews, quality assurance, and ongoing system improvement.

Required Qualifications, Capabilities, and Skills:

• Bachelor’s degree in Computer Science, Software Engineering, or related field.
• Commercial software development experience, ideally with exposure to ML/AI systems.
• Strong programming skills in Python; familiarity with web frameworks (Flask, FastAPI).
• Understanding of model inference lifecycles, APIs, and data validation.
• Familiarity with Git, CI/CD pipelines, testing, and performance profiling.
• Ability to work independently and deliver clean, maintainable, production-quality code.

Preferred Qualifications, Capabilities, and Skills:

• Master’s degree or certifications in ML engineering, MLOps, or cloud infrastructure.
• Familiarity with data cataloging, tagging, or schema inference workflows.
• Exposure to enterprise governance, compliance, or secure access systems.
• Interest in explainable AI, decision support tooling, and intelligent policy engines.