JP Morgan Chase

Runs a consumer bank and an investment bank

AI/ML Python Developer

AI Application EngineeringConfirmed live in the last 24 hours

Full-timeGlasgow, UKNo salary listedIn-person

About the Job

Requirements

Formal training or certification in software engineering concepts, plus 5+ years of applied experience building production Python systems, including web/API services (Flask or FastAPI) and the machine learning/natural language processing ecosystem (scikit-learn, pandas, NumPy).
Demonstrated experience taking machine learning models from prototype to production, including training, packaging, deployment, monitoring, retraining, and decommissioning in real-world business applications.
Experience building large language model/small language model-powered applications, including retrieval-augmented generation-based systems, summarization/extraction pipelines, chat/coplay experiences, and tool-using agents.
Proven experience working with large datasets and distributed compute (Spark, Databricks, or equivalent), with SQL fluency and an understanding of partitioning, performance, and cost.
Hands-on experience using enterprise-authorized AI-assisted software development tools in the work environment for coding, test creation, troubleshooting, or documentation, and ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs and outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
Working knowledge of large language model application patterns, including prompt design, retrieval-augmented generation, embeddings and vector search, structured output, and tool/function calling.
Familiarity with Model Context Protocol, Agent to Agent (A2A), Agent Skills, and architectures that connect models to tools or data through standardized interfaces.
Overall knowledge of the Software Development Life Cycle and a solid understanding of agile delivery practices, including continuous integration/continuous delivery, application resiliency, and security.

Responsibilities

Execute software solutions, design, development, and technical troubleshooting, thinking beyond routine or conventional approaches to build solutions or break down technical problems.
Create secure, high-quality production code and maintain algorithms that run synchronously with appropriate systems.
Leverage enterprise-authorized AI coding assistance tools to improve code quality, delivery speed, and productivity across complex deliverables, including code generation/refactoring, unit test creation, and documentation; validate outputs through peer review, automated testing, and secure coding standards; contribute learnings and reusable patterns to improve broader team effectiveness.
Apply knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
Produce architecture and design artifacts for complex applications and ensure design constraints are met by software code development.
Gather, analyze, synthesize, and develop visualizations and reporting from large, diverse datasets to support continuous improvement of software applications and systems.
Proactively identify hidden problems and patterns in data and use these insights to improve coding hygiene and system architecture.

Desired Qualifications

Production experience with Databricks (Delta Lake, Unity Catalog, MLflow, and Databricks Jobs) and workflow orchestration with Apache Airflow.
Experience with evaluation frameworks and approaches, including golden datasets, LLM-as-judge, human-in-the-loop review, and red teaming.
Grounding in data preprocessing, feature engineering, model selection, hyperparameter tuning, and evaluation, including choosing appropriate metrics for imbalanced and unlabeled problems.
Experience with AWS Bedrock, SageMaker or equivalent managed machine learning/generative AI platforms, ECS, and deployment patterns for scalable inference.
Experience with developer productivity tools such as GitHub Copilot and Claude Code, paired with strong Software Development Life Cycle controls.
Knowledge of the financial services industry and operating in regulated environments, including auditability, controls, and data handling.
Familiarity with financial risk domain concepts, including market, credit, counterparty, or investment risk, portfolio exposure, and data-quality controls.

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.

As a Software Engineer III at JPMorganChase within the Corporate Technology, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture

Required qualifications, capabilities, and skills

Formal training or certification in software engineering concepts, plus 5+ years of applied experience building production Python systems, including web/API services (Flask or FastAPI) and the ML/NLP ecosystem (scikit-learn, pandas, NumPy).
Demonstrated experience taking machine learning models from prototype to production - training, packaging, deployment, monitoring, retraining, and decommissioning - in real-world business applications.
Experience with building LLM/SLM-powered applications including RAG-based systems, summarization/extraction pipelines, chat/coplay experiences, and tool-using agents.
Proven experience working with large datasets and distributed compute (Spark / Databricks or equivalent), with SQL fluency and an understanding of partitioning, performance, and cost.
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
Working knowledge of LLM application patterns - prompt design, retrieval-augmented generation (RAG), embeddings and vector search, structured output, and tool/function calling.
Familiarity with MCP (Model Context Protocol), Agent to Agent(A2A) Agent Skills and architectures that connect models to tools/data through standardized interfaces.
Overall knowledge of the Software Development Life Cycle, and a solid understanding of agile delivery practices including CI/CD, Application Resiliency, and Security.

Preferred qualifications, capabilities, and skills

Production experience with Databricks (Delta Lake, Unity Catalog, MLflow, Databricks Jobs) and workflow orchestration with Apache Airflow.
Experience with evaluation frameworks and approaches (golden datasets, LLM-as-judge, human-in-the-loop review, red teaming).
Solid grounding in data pre-processing, feature engineering, model selection, hyper-parameter tuning, and evaluation, including choosing appropriate metrics for imbalanced and unlabeled problems.
Experience with AWS Bedrock, SageMaker (or equivalent managed ML/GenAI platforms), ECS and deployment patterns for scalable inference.
Experience with developer productivity tooling such as GitHub Copilot and Claude Code, paired with strong SDLC controls.
Knowledge of the financial services industry and operating in regulated environments (auditability, controls, data handling).
Familiarity with financial risk domain concepts - market, credit, counterparty, or investment risk, portfolio exposure, and data-quality controls.

About the Company

JP Morgan Chase

Runs a consumer bank and an investment bank

JPMorgan Chase runs the largest bank in the United States, which serves households and small businesses as Chase and large companies, governments, and investors as J.P. Morgan. During the 2008 financial crisis it bought the failing investment bank Bear Stearns and lender Washington Mutual, and in 2023 it took over First Republic Bank after regulators seized it. Chase takes deposits and earns interest on credit cards, mortgages, and car loans, while the investment side earns fees for advising on mergers, raising money for companies, trading stocks and bonds, and managing wealth. Formed in a 2000 merger, JPMorgan Chase traces its roots to 1799 and is based in New York City.

Company size: 10,001+
Company stage: IPO
Headquarters: New York City, New York
Founded: 1799

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