Live opening · Posted 11 days ago
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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 JPMorgan Chase within the Employee Platforms team, you will be responsible for ensuring the reliability, scalability, and automation of AI-powered applications and infrastructure. In this role, you will be responsible for ensuring the reliability, scalability, and automation of AI-powered applications and infrastructure. You will partner with engineering, and other stakeholders to deliver modern observability, intelligent incident response, and autonomic operations across our applications.
Job responsibilities
Ensure reliability, scalability, and performance of AI-assisted application and platform operations.
Design and implement AI-driven solutions for intelligent alerting, noise reduction & auto-correlation systems..
Build and maintain observability, monitoring, and telemetry for AI application and platforms.
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.
Build and support automation for alerting, anomaly detection, and self-healing workflows.
Collaborate with engineering, and other stakeholders to drive operational excellence.
Mentor and guide engineers on AIOps standards and operational excellence.
Define and execute the roadmap for AI-assisted SRE and observability.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
Demonstrates strong experience in SRE, DevOps, or Platform Engineering roles.
Strong hands-on experience with AWS (ECS, Lambda, API Gateway, Bedrock, CloudWatch, RDS, EKS).
Hands-on experience with AWS Bedrock, OpenAI, or LLM APIs.
Expertise in observability tools: OpenTelemetry, Grafana, Prometheus, ELK, CloudWatch.
Experience with CI/CD tools (GitHub Actions, Jenkins, Spinnaker ).
Proven track record in automation, operational tooling, and event-driven workflows.
In-depth understanding of distributed systems, microservices, and cloud architectures.
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
Preferred qualifications, capabilities, and skills
Experience with AI-powered coding assistants like GitHub Copilot, windsurf.
Familiarity with prompt engineering, embeddings, and RAG pipelines.
Experience building operational copilots or chatbots for runbooks or troubleshooting.
Proficiency in Python (Go is a plus).
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