JPMorgan Chase
Lead Software Engineer - Java, Spring Boot, Python, PySpark, Databricks, AWS
Plano, TX +1 · Full-time · Posted Oct 12, 2026
- Location
- Plano, TX
Wilmington, DE - Experience
- 5+ years of applied experience; 2+ years leading technologists.
- Education
- Formal training and certification on software engineering concepts.
About the job
Leads software engineering and architecture for risk technology products, building and operating secure, scalable applications and data solutions while promoting AI-assisted engineering practices.
What you'll do
- Designs, develops, troubleshoots, and maintains production software and complex system architectures.
- Drives adoption and governance of AI-assisted engineering and AI/ML delivery practices, with validation standards for quality, security, and performance.
- Analyzes large datasets and uses insights to improve applications, data pipelines, coding practices, and system architecture.
What they ask for
- Formal software engineering training and certification, with 5+ years of applied experience and 2+ years leading technologists.
- Hands-on experience in system design, application development, testing, and operational stability, including developing and maintaining code in a large corporate environment.
- Experience with programming languages and frameworks for data engineering or machine learning; strong SQL skills and experience optimizing Spark clusters.
- Hands-on use of enterprise-authorized AI-assisted development tools; experience with AWS services including EMR, EC2, S3, and CloudWatch, and working knowledge of Databricks.
Nice to have
- Experience with Java, Python, AWS/Databricks, and high-volume, high-availability, low-latency Java Spring Boot APIs.
- Awareness of Python machine learning libraries, AWS certifications, Apache Airflow, or modern LLM techniques.
Tools
Java, Python, PySpark, SQL, Spark, AWS, EMR, Databricks
AI focus
Promotes governed AI-assisted engineering across the software lifecycle, including code review and refactoring, test acceleration, release readiness, and incident/root-cause analysis; also drives AI/ML delivery practices and works with machine learning models.
Model-written summary · the original posting is authoritative