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Lead Software Engineer
lead · Technology / Software Development
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 Infrastructure Platforms Foundational Services (IPFS) organization, 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 in the Central Site Reliability Engineering (SRE) team, you will design and build the tools and automation that improve the reliability and operability of critical infrastructure, and you will work closely with our Foundational Services (FS) domain partners.
Job responsibilities
- Executes creative software solutions, design, development, and technical troubleshooting with the 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
- Builds and maintains tooling and automation that reduce operational toil and improve the stability of infrastructure platforms and services
- Partners with the FS domain partners to implement observability and reliability capabilities (e.g., metrics, logging, tracing, alerting, and SLI/SLO instrumentation)
- Drives team adoption of enterprise-authorized AI-assisted engineering practices (e.g., code review/refactoring, test acceleration, incident/root-cause analysis) across the SDLC toolchain, establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns
- 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
Required qualifications, capabilities, and skills
- Formal training or certification in software engineering concepts with 7+ years of applied experience in Software Engineering, DevOps, or Site Reliability Engineering
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Advanced proficiency in one or more programming languages (e.g., Java, Go, or Python)
- Demonstrated experience leading effective, responsible use of approved AI-assisted development tools — setting team expectations for validating outputs (correctness, performance, security), handling data sensitivity appropriately, and coaching engineers on safe, compliant adoption
- 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
- In-depth knowledge of the financial services industry and their IT systems
- Practical cloud-native experience
- Experience developing observability solutions or integrating with tools such as Grafana, Prometheus, Splunk, and infrastructure/network monitoring.
- Experience with on-premises data-center infrastructure, networking, storage, or data protection/replication
- Familiarity with Site Reliability Engineering principles (SLIs/SLOs, error budgets, toil reduction) and partnering with SRE teams
- Experience with infrastructure as code (Terraform) and GitOps workflows
- Experience with containerization and orchestration (Docker, Kubernetes)
- Experience building RESTful APIs and event-driven integrations (Kafka, RabbitMQ, SQS)
- Hands-on experience building AI agents and autonomous systems with AI frameworks (LangChain, LangGraph, AutoGen, CrewAI), and leveraging AI development tools (GitHub Copilot, Claude, etc.) to accelerate development
- Experience designing logging pipelines (Fluentd, Logstash, Vector) and systems for metrics collection, analysis, and distributed tracing
- Familiarity with graph databases (Neo4j, TigerGraph), vector databases (Pinecone, Weaviate, Chroma), and integrating multiple data stores for AI-powered systems
- Contributions to open-source projects or engineering communities of practice