The Applied Innovation (of AI) Engineering team is an elite machine learning group strategically located within the Chief Data Analytics Office (CDAO) Technology group of JP Morgan Chase. AI Engineering tackle business critical priorities using innovative machine learning techniques and technologies with a focus on machine learning for Software, Cybersecurity and Technology Infrastructure. The team partners closely with all lines of business and engineering teams across the firm to execute long-term projects in these areas that require significant machine learning development to support JPMC businesses as they grow.
As a Principal Software Engineer at JPMorganChase within CDAO Technology Group in the Applied Innovation (of AI) Engineering team, you will design, develop, deploy, and maintain advanced AI products. You’ll collaborate with software engineers, data engineers, and data scientists to deliver trusted solutions. Your role is central to executing long-term projects that drive innovation and support our business growth. You’ll contribute to a culture of inclusion and technical excellence.
Job Responsibilities
- Collaborates with engineers, data scientists, and product owners, to deliver products to production.
- Builds and maintain data pipelines for analytics, model evaluation, and training (includes versioning, compliance and validation).
- Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
- Provides feedback and proposes improvements to architecture governance practices
- Creates secure and high-quality production code, and reviews and debugs code written by others. Maintains algorithms that run synchronously with appropriate systems
- Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
- Regularly provides technical guidance and direction to support the business and its technical teams and vendors
- Architects and governs agentic AI-enabled engineering workflows (using enterprise-authorized tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI-driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root-cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams
- 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 at scale
Required Qualifications, Capabilities and Skills
- Proven programming/scripting skills with Python in an AWS /Cloud environment
- Experience in the use of GPT, LLM, RAG, Gen AI, Prompt Engineering related technologies
- Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
- Current experience of agile methodologies such as CI/CD, Applicant Resiliency, and Security
- Solid understanding of software applications and technical processes within a related technical discipline (e.g. Synchronous and non-synchronous APIs, Concurrency, etc.)
- Creates complex and scalable coding solutions using appropriate software design frameworks
