Lead Software Engineer - Full Stack Developer

JPMorganChase•Plano, TX

About The Position

This role involves executing creative software solutions, encompassing design, development, and technical troubleshooting. The engineer will be responsible for developing secure, high-quality production code, reviewing and debugging code written by others, and driving team adoption of enterprise-authorized AI-assisted engineering practices. This includes improving code quality, delivery speed, and operational outcomes through AI-assisted code review, refactoring, test strategy acceleration, and incident analysis. The role also involves establishing consistent validation standards (secure coding, peer review, automated testing) and promoting the reuse of effective patterns. The engineer will apply knowledge of the Software Development Life Cycle (SDLC) toolchain, including AI-assisted development and automation capabilities, to enhance value realized by automation. The position leads the end-to-end delivery of full-stack features using ReactJS UI, Java/Python backend services, SQL persistence, and Kafka event streaming. A key aspect is driving spec-driven development by authoring, reviewing, and maintaining executable/validated specifications (APIs, events, data contracts) as the source of truth, ensuring implementation and tests align with the spec. AI-assisted development frameworks will be used to accelerate requirements-to-spec, spec-to-implementation, and spec-to-test workflows while maintaining engineering rigor. The role includes designing and implementing scalable, resilient REST APIs with clear interface contracts and backward compatibility strategies, as well as AWS-based components leveraging S3 and AWS Lambda. Building and operating containerized services using Docker and Kubernetes, implementing event-driven workflows with Kafka, and provisioning/managing environments with Terraform are also core responsibilities. Additionally, the role involves building LLM-based implementations (e.g., RAG, tool/function calling) with appropriate evaluation, monitoring, and controls, and establishing SDLC standards (design reviews, code reviews, automated testing, CI/CD, documentation, operational readiness). Mentoring and coaching engineers, providing technical direction, and promoting continuous improvement are also expected.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience
Hands-on practical experience delivering system design, application development, testing, and operational stability
Demonstrated experience leading effective use of approved AI-assisted software development tools (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 engineers on safe, compliant adoption within delivery practices
Strong backend development experience with Python (e.g., FastAPI/Flask), including API and service development.
Strong frontend development experience with ReactJS (modern React patterns, TypeScript/JavaScript).
Hands-on experience with Docker and Kubernetes running production workloads.
Experience with Kafka (producer/consumer patterns, error handling, schema evolution).
Strong SQL skills and relational data modeling experience; query optimization and indexing.
Hands-on AWS experience including S3 and AWS Lambda; cloud security fundamentals (e.g., IAM-based access patterns).
Experience using Terraform for infrastructure-as-code.
Spec-driven development experience, including: Defining and maintaining machine-readable specs (e.g., OpenAPI for REST and/or AsyncAPI for event interfaces); Driving implementation and automated tests from specs (contract testing, validation, backward compatibility checks); Using AI-assisted spec-to-code / spec-to-test tooling (e.g., OpenSpec, Spec-Kit, or similar).
LLM application experience in production (prompt/versioning practices, grounding/RAG concepts, evaluation/monitoring basics, secure data handling).

Nice To Haves

Experience working with relational databases such as DB2 or PostgreSQL.
Demonstrated experience leading effective use of approved AI-assisted software development tools (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.
Financial services knowledge, with an understanding of the commercial lending space.
Experience working in regulated environments requiring strong risk management, controls, and auditability.

Responsibilities

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.
Develops secure high-quality production code, and reviews and debugs code written by others.
Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
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.
Lead end-to-end delivery of full-stack features spanning ReactJS UI, Java/Python backend services, SQL persistence, and Kafka event streaming.
Drive spec-driven development by authoring, reviewing, and maintaining executable/validated specifications (APIs, events, data contracts) as the source of truth; ensure implementation and tests remain aligned to the spec.
Use AI-assisted development frameworks (e.g., OpenSpec, Spec-Kit, or similar) to accelerate requirements-to-spec, spec-to-implementation, and spec-to-test workflows while maintaining engineering rigor.
Design and implement scalable, resilient REST APIs, including clear interface contracts and backward compatibility strategies.
Design and implement AWS-based components leveraging S3 and AWS Lambda where appropriate.
Build and operate containerized services using Docker and Kubernetes (deployments, services/ingress, configuration/secrets, health checks, autoscaling).
Implement event-driven workflows using Kafka (topic strategy, consumer groups, retry/DLQ patterns, schema evolution).
Provision and manage environments using Terraform (repeatable infrastructure, safe change management).
Build LLM-based implementations (e.g., RAG, tool/function calling) with appropriate evaluation, monitoring, and controls.
Establish SDLC standards: design reviews, code reviews, automated testing, CI/CD, documentation, and operational readiness.
Mentor and coach engineers; provide technical direction and promote continuous improvement.