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Software Engineer III- SRE
senior · Technology / Software Development
We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.
As a Software Engineer III at JPMorganChase within the Enterprise technology, Corporate technology team , you hold a leadership role in your team, demonstrate strong knowledge across multiple technical domains, and advise others on the technical and business issues facing them. Take lead and conduct design reviews, break up complex problems into digestible work for other engineers, act as a technical lead for medium to large-sized products, and provide advice and mentoring to other engineers.
Job 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
- Uses enterprise-authorized AI capabilities within the work environment to accelerate reliability design and operational decisioning (e.g., incident/post-incident analysis and requirements traceability), validating outputs and handling operational data according to sensitivity and security requirements.
- Develops secure high-quality production code, and reviews and debugs code written by others
Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems - Demonstrates a high level of technical expertise within one or more technical domains and proactively identifies and solves technology-related bottlenecks in your areas of expertise
- Acts as the main point of contact during major incidents for your application and demonstrates the skills to identify and solve issues quickly to avoid financial losses
- Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
- 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.
- Documents and shares knowledge within your organization via internal forums and communities of practice
- Leads reuse-first adoption of AI-assisted reliability workflows across SDLC/toolchain practices (e.g., testing/validation automation and production readiness), ensuring traceability/auditability, resiliency, and security controls.
Adds to team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
- Formal training or certification in software engineering concepts plus 5 years of applied experience
- Proficiency in reliability, scalability, performance, security, toil reduction and site reliability best practices with the ability to implement these practices within an application or platform
- Demonstrated experience using enterprise-authorized AI capabilities within the work environment
- Ability to set team practices for safe AI usage in operations
- Fluency in at least one programming language such as (e.g., Python, Java Spring Boot, .Net, etc.)
- Experience and exposure to observability tools and telemetry collection using tools such as Grafana, Dynatrace, Prometheus, Datadog, Splunk, etc.
- Proficiency in automation and continuous delivery methods
- Experience with troubleshooting common networking technologies and issues
- Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
- Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
Preferred qualifications, capabilities, and skills
- Ability to identify and solve problems related to complex data structures and algorithms
- Drive to self-educate and evaluate new technology
- Ability to expand and collaborate across different levels and stakeholder groups
- Practical cloud native experience