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Principal Software Engineer – Insurance
lead · Technology / Software Development
JPMorgan Chase is on a journey to be the most recommended financial partner for small business owners in our communities as they start, run, and grow their business. This role is an opportunity to be at the forefront of building a best-in-class, integrated, digital-led P&C Insurance business within a leading financial services brand – focused on deepening relationships with our >7MM small business clients.
As a Principal Software Engineer at JPMorgan Chase within the Consumer & Community Bank, you will serve as the hands-on technical leader for a new AI-native insurance platform—designing core systems, writing production code, and establishing technical patterns and practices that will scale across the engineering organization. You bring deep expertise in agentic workflow design, LLM integration patterns, and AI-assisted development, and you lead by example—influencing adoption through technical excellence and coaching.
Job Responsibilities
- Architect and build the core technical systems for a digital insurance platform, including policy management, quoting engines, carrier integrations, and AI-powered customer experiences—writing production code for critical components and establishing patterns for the broader team
- Design and implement agentic workflows and LLM-powered features, establishing technical standards and integration patterns that enable AI-native development across the platform
- Promote adoption of AI-augmented engineering practices through hands-on technical leadership—conducting architecture and code reviews, pairing with engineers, and coaching teams on AI coding tools, agentic design patterns, and modern development workflows
- 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
- Identify and mitigate technology-related risks by implementing robust security measures, ensuring compliance with regulatory standards, and building systems for operational resiliency and stability
- Collaborate with architects, product owners, and engineering teams to evaluate emerging technologies, identify opportunities for technical improvement, and promote adoption of modern frameworks and tools
- Stay current on emerging AI coding technologies, agentic system patterns, and software engineering practices, and proactively introduce innovations that accelerate delivery and improve product quality
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 10+ years applied experience
- Expert-level proficiency with AI coding assistants (GitHub Copilot, Cursor, Claude, etc.) and demonstrated ability to coach teams in their adoption and effective use
- Experience designing and implementing agentic systems (e.g., LLM-powered agents, multi-step reasoning workflows, tool-using AI) in production environments
- Champions an AI-augmented engineering culture, setting technical standards, running experiments, and building team confidence as tooling and best practices evolve in real time
- Experience building or integrating digital insurance platforms, including policy administration, quoting systems, carrier APIs, or agency management tools
- Hands-on practical experience delivering system design, application development, testing, and operational stability for large-scale digital platforms
- Demonstrated experience designing and leading adoption of agentic AI-enabled development practices (using enterprise-authorized tools within the work environment) across teams, including setting standards for human-in-the-loop validation, auditability/traceability of changes, and secure handling of sensitive data
- Strong understanding of responsible AI use and control expectations in engineering workflows, including security/resiliency implications, data sensitivity, and risk-based governance; ability to influence senior technical leaders on safe scaling patterns and reuse.
- Expert in one or more programming languages (e.g., Java, Python, Node.js) with deep understanding of modern software development practices
- Expert knowledge and direct experience building cloud-native applications using Kubernetes, service mesh, Kafka, API gateways, and SQL/NoSQL databases
- Advanced knowledge of software application development and technical processes with considerable in-depth knowledge in one or more technical disciplines (e.g., cloud, artificial intelligence, distributed systems, etc.)
Preferred qualifications, capabilities, and skills
- Experience applying expertise and new methods to determine solutions for complex technology problems in one or more technical disciplines
- Ability to present and effectively communicate with senior leaders and executives Consolidate bullet points in this section
- Practical cloud native experience with deep expertise in specific technologies such as PostgreSQL, distributed SQL databases, Kubernetes native features, and observability tools (e.g., Dynatrace)