✨ AI Summary
As a Manager of Software Engineering, you will lead the design, development, and delivery of innovative software solutions, solving complex problems with creative approaches. You will build and maintain high-quality, secure code, mentor engineers, and drive architecture decisions for scalability and resiliency. The role involves identifying automation opportunities, developing data-intensive solutions using Java/Scala, and designing AI-enabled applications. You will lead the adoption of enterprise-authorized AI-assisted engineering practices and SDLC automation to improve delivery speed and quality, working across hybrid data ecosystems like AWS, Databricks, and Kubernetes. Ensuring code quality through testing and collaborating with cross-functional teams are key responsibilities. Requires 10+ years of software engineering experience, proficiency in Java, Spring Boot, SQL, modern front-end frameworks, and experience with distributed systems, AI-assisted development, CI/CD, containerization, cloud platforms, and Generative AI systems.
Requirements
Requires 10+ years of software engineering experience with expertise in Java, Spring Boot, SQL, and modern front-end frameworks. Must have experience designing scalable, secure, and resilient distributed systems, understanding modern architecture patterns, leading AI-assisted development adoption, and working with CI/CD, containerization, and cloud platforms. Experience with Generative AI systems, API design, and integration patterns is essential.
Description
Join us to advance your software engineering career while building impactful technology solutions. Grow your skills and make a difference with a collaborative team.
As a Manager of Software Engineering at JPMorgan Chase within Corporate Technology , you will lead the design, development, and delivery of innovative software solutions.
Job Responsibilities
- Lead the design, development, and delivery of innovative software solutions, solving complex problems with creative and forward-thinking approaches
- Build and maintain high-quality, secure, and production-grade code, while mentoring and reviewing the work of other engineers
- Drive architecture decisions to ensure scalability, resiliency, and zero-downtime deployments
- Identify opportunities to automate operational processes and eliminate recurring issues, improving platform stability and efficiency
- Develop and implement data-intensive and statistical solutions using Java/Scala on large-scale datasets
- Design and build AI-enabled applications, including AI Agents and agentic workflows
- Leads team adoption of enterprise-authorized AI-assisted engineering practices and SDLC/TLM automation to improve delivery speed, quality, and operational outcomes, while setting expectations for human validation, secure handling of inputs/outputs, and consistent use of reusable patterns 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 and support capacity unlock initiatives.
- Work across hybrid data ecosystems (AWS, Databricks, Kubernetes)
- Ensure code quality through unit testing, integration testing, and continuous validation practices
- Collaborate with cross-functional teams to deliver end-to-end solutions aligned with enterprise standards
Required Qualifications, Capabilities, and Skills
- 10+ years of hands-on experience in software engineering with formal training or equivalent practical expertise
- Strong proficiency in Java and Spring Boot, with working knowledge of SQL and modern front-end frameworks (e.g., React)
- Demonstrated experience in designing scalable, secure, and resilient distributed systems
- Strong understanding of modern architecture patterns (microservices, event-driven, API-first)
- Experience leading responsible adoption of enterprise-authorized AI-assisted development and delivery tools across engineering teams, including defining ways of working (review/validation expectations), measuring outcomes, and ensuring secure handling of data.
- Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and governance expectations; ability to coach engineers on compliant and effective usage.
- Experience with CI/CD pipelines, containerization (Docker, Kubernetes/ECS/EKS), and Infrastructure as Code
- Expertise in API design (REST, GraphQL) and integration patterns
- Hands-on experience building applications on AWS or other public cloud platforms
- Experience with Generative AI systems, including: (Retrieval-Augmented Generation) architecture, AI orchestration frameworks (LangChain, LlamaIndex, etc.) and LLM evaluation frameworks and responsible AI practices (guardrails, content filtering)
Preferred Qualifications, Capabilities, and Skills
- Deep expertise in Java, object-oriented design, and system design
- Experience with ElasticSearch, Oracle, MongoDB, and modern data platforms
- Proven experience building AI-enabled applications and agentic architecture
- Experience developing reusable AI Agent skills/frameworks to accelerate engineering teams