Lead Software Engineer - Observability Platform Cloud Developer

JPMorgan Chase & Co.•Wilmington, DE

10d

About The Position

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Lead Software Engineer at JPMorganChase within the Enterprise Observability Platforms, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

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.
Advanced in one or more programming language(s) (e.g, Java, JavaScript, React, Python).
Advanced experience working with two or more from the following: web application development, database, Unix/Linux environments, distributed and parallel systems, information retrieval, networking, large scale software development, security software development, Kafka, Kubernetes, Microservices, Terraform.
Proficiency in automation and continuous delivery methods.
Proficient in all aspects of the Software Development Life Cycle.
Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security.
Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.).
Practical cloud native experience, with cloud application deployment (AWS certification).
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

Nice To Haves

Experience with IBM Netcool/OMNIbus and related products.
Experience with Cloud based AIOPs applications (e.g. IBM CloudPak for AIOPs, SerivceNow).
Experience and Knowledge of Observability Products (e.g. Events and Alerts, SCOM, SMARTS, IBM Tivoli).
Knowledge of automation and scripting languages (e.g. Python, Ansible).
Experience with related tools (e.g. IBM Tivoli, IBM Netcool Suite, ServiceNow, Splunk, Oracle, DynaTrace).

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.
Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems.
Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture.
Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies.
Adds to team culture of diversity, opportunity, inclusion, and respect.
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.