Software Engineer III - AI

JPMorgan Chase & Co.; London; On-site; GBP 55,000 - 75,000; Full time

Job summary

A global financial services firm in London seeks a skilled software developer to join their Chief Data & Analytics Office. The role involves building and integrating machine learning models, writing production-ready Python code, and collaborating with cross-functional teams. Candidates should have a strong background in software development with a focus on ML/AI systems, alongside qualifications in Computer Science. This position offers an exciting opportunity to influence the firm's data ecosystem with innovative solutions.

Qualifications

• 2+ years of 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.

Responsibilities

• Build and integrate ML models into structured backend services.
• Write production-ready Python code to support model inference, validation, and logging.
• Assist in building automated workflows for data ingestion and model deployment.

Skills

ML model integration

Production-ready Python coding

Automated workflows

Collaborative development

Education

Bachelor’s degree in Computer Science

Tools

Python

Git

Flask

Job description

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Join the Chief Data & Analytics Office (CDAO) at JPMorgan Chase and be part of a team that accelerates the firm's data and analytics journey. We focus on ensuring data quality and security while leveraging insights to promote decision-making and support commercial goals. Our mission is to modernize compliance using scalable AI, creating systems that intelligently automate data usage decisions. This role offers the chance to address complex challenges across the firm's data ecosystem and develop impactful machine learning solutions.

When someone joins our team within CDAO, they become part of a mission to modernize compliance through scalable and explainable AI. We are building a system that answers the question: “Can I use this data?”, not with guesswork, but with prediction/classification, logic, proof, and intelligent automation.

Our work sits at the intersection of applied machine learning, AI reasoning systems, and data governance. We are designing the triage layer of an intelligent decision engine that combines ML-driven classification, LLM-assisted parsing, and formal logic-based verification. This is an opportunity to tackle complex, ambiguous problems that touch every part of the firm’s data ecosystem and to build ML solutions that actually make decisions.

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
• 2+ years of 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