Senior Lead Software Engineer - Azure DevOps

Drive significant business impact and tackle a diverse array of challenges that span multiple technologies and applications

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products. As a Senior Lead Software Engineer - Azure DevOps at JPMorganChase within the Asset and Wealth Management, 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. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications. Job responsibilities Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors Develops secure and high-quality production code, and reviews and debugs code written by others Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain. Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale. Drives decisions that influence the product design, application functionality, and technical operations and processes Serves as a function-wide subject matter expert in one or more areas of focus Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and 5+ years applied experience Proven Azure cloud expertise supporting mission-critical production environments. Strong hands-on experience with modern development languages ( Python, Java, Shell scripting ) and engineering practices/tools including Git , CI/CD , Infrastructure as Code (IaC) , Terraform , and Jenkins . Experience designing and building RESTful APIs , including integration with event-driven and/or serverless architectures. Strong understanding of containerization and orchestration technologies, including Docker , Kubernetes , GKE , and Helm . Hands-on experience with cloud deployment, monitoring, and operations, using tools such as Azure Monitor , Datadog , Prometheus , Splunk , Elasticsearch , and Grafana . Demonstrated experience using enterprise-authorized AI capabilities to improve SRE workflows (e.g., incident investigation support and knowledge capture), with strong validation habits and awareness of data sensitivity. Ability to evaluate AI-assisted operational recommendations for correctness and risk, define appropriate guardrails for team usage, and ensure outcomes align with resiliency and security expectations. Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls. Preferred qualifications, capabilities, and skills: Working knowledge of operating systems, including Windows and Linux (Red Hat / Ubuntu). Familiarity with LLMs and broader AI/ML frameworks applicable to AIOps use cases. Working knowledge of agentic AI SDKs and GitHub Copilot skills/capabilities. Public cloud certification or equivalent depth of technical experience in public cloud platforms.