Lead Software Engineer - Python, Data, Cloud, AIML

JPMorgan Chase & Co.; Greater London; On-site; GBP 120,000 - 180,000; Full time

Job summary

JPMorgan Chase & Co. in London is seeking a Lead Software Engineer to push the boundaries of cloud-native data, backend engineering, and AIML at production scale. You will design, build, and govern secure systems within the Markets Research Technology team, driving AI-assisted practices and scalable microservices.

You will guide development across data engineering, cloud infra, and MLOps, mentoring teams and shaping robust software solutions for the firm’s business objectives.

Qualifications

• Formal training or certification on software engineering concepts.
• Hands-on design, development, testing, and operational stability.
• Proficient in Python and modern programming languages.
• Experience leading enterprise AI-assisted software development tools and practices.
• Strong understanding of secure, scalable microservices and distributed systems.
• Experience with cloud services, Infrastructure as Code, containers, and big data technologies.

Responsibilities

• Executes software solutions, design, development, and troubleshooting with non-routine approaches.
• Advances AI-assisted engineering practices across teams to improve quality and delivery.
• Utilizes SDLC toolchains and AI-enabled development for scalable automation.
• Produces secure production code and maintains production-ready algorithms.
• Creates architecture and design artifacts for complex, data-intensive apps.
• Builds data, backend, cloud infra, and MLOps stacks for Data and AIML products.
• Implements data engineering solutions using modern big data tech.
• Contributes to engineering communities of practice and explores new tech.
• Maintains a learning mindset and proactive problem-solving attitude.

Skills

Python

AI/ML concepts

Microservices architecture

Distributed systems

Big data technologies

Tools

Kubernetes

EKS

Docker

MLOps

Job description

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

As a Lead Software Engineer at JPMorgan Chase within theCommercial & Investment Bank's Markets Research Technologyteam,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. You will work on challenging Cloud-native data, backend engineering and AIML engineering,helping usindustrialize AI/ML models at Production scale. This role is a technical hands-on Engineering role. Experience with data science/ML modeling is advantageous but not essential to this role.

Job responsibilities

• Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
• Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
• Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
• Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
• Builds engineering stack required for Data and AIML products, including data engineering, backend engineering, Cloud infra DevOps and MLOps
• Designs and implements data engineering solutions, leveraging modern big data technologies
• Contributes to software engineering communities of practice and events that explore new and emerging technologies
• Embraces a passion for learning, problem-solving, creative thinking and a can-do attitude.

Required qualifications, capabilities, and skills

• Formal training or certification onsoftware engineeringconcepts and proficient applied experience
• Hands-on practical experience in system design, application development, testing, and operational stability
• Proficient in coding in one or more languages- Python
• Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
• Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls.
• Proven track record in system design, architecting and developing microservices, distributed systems and data-intensive applications
• Experience with Cloud services, Infrastructure as Code, containerized application development, big data and modern data engineering technologies
• Practical experience developing Production-scale Cloud-native data engineering solutions in commercial environments
• Familiarity with Cloud Data engineering services (e.g., ETL, Glue, S3, Athena) and MLOps stack
• Ability to convey design choices and results clearly and communicate effectively to stakeholders of various backgrounds and Overall knowledge of the Software Development Life Cycle

Preferred qualifications, capabilities, and skills

• Experience with data, AWS and AIML engineering in commercial settings, preferably in financial sector
• Experience working on LLM applications or other AI/ML systems
• Practical experience with Kubernetes, EKS, Docker, MLOps
• Prior exposure to LLMs, RAG, Knowledge Graph Technologies, OpenSearch and vector databases
• Prior experience collaborating with data scientists