The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firm’s data and analytics journey. This includes ensuring the quality, integrity, and security of the company's data, as well as leveraging this data to generate insights and drive decision-making. The CDAO is also responsible for developing and implementing solutions that support the firm’s commercial goals by harnessing artificial intelligence and machine learning technologies to develop new products, improve productivity, and enhance risk management effectively and responsibly.

As a Lead Software Engineer at JPMorgan Chase within the AI Research team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. This is a hands-on technical leadership role, and you will work with AI Researchers across any number of fields including Explainability (XAI), Fairness, Optimization, Synthetic Data, Cryptography and Markets/Trading Research (and many more). Whilst working closely with the researchers, your core deliveries will centre around the technology required to make AIML innovations and research operate at scale within a top tier investment and retail banking businesses. 

The AI Research team applies novel AI techniques to the toughest challenges in financial services. We are looking for an experienced developer to help evolve projects from early-stage code into production. 

Job responsibilities 

Leverages and integrates the latest Gen AI advances into the firm’s workflow in a scalable and secure manner Applies AI techniques to accelerate critical technology modernization programs (addressing productivity and privacy issues) Integrates these seamlessly into the firm’s business systems, withing the constraints of a highly structured and opinionated technology environment Creates robust pipelines for repeatable model delivery

Required qualifications, capabilities, and skills 

Formal training or certification on Computer Science concepts and 5+ years applied experience Exceptional Cloud Engineering skills (both public and private), and a proven track record of navigating a complex technical environment and delivering robust solutions within those constraints Up to date understanding of Model Development Lifecycle, and best practices for managing the fast-moving model provisioning environment Strong track record of developing high quality, production code in Python Advanced understanding of engineering methodologies such as CI/CD, Application Resiliency, and Security Great interpersonal skills and able to interface with data scientists, quantitative researchers and other engineers Strong understanding of modern development practices

Preferred qualifications, capabilities, and skills

Experience in AI an advantage, interest in AI a must! Experience with AWS and Kubernetes  Track record of Big Data specific infrastructure (e.g. Spark)  Interest / understanding of statistics and optimization techniques  Experience integrating new tools/libraries into frameworks