About The Role

As a Lead Software Engineer at JPMorganChase Commercial and Investment Banking, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job Responsibilities

Design, build, and operate scalable cloud infrastructure with a strong focus on reliability, security, and operability
Develop and maintain Infrastructure as Code (IaC) patterns (reusable modules, consistent standards, safe deployments)
Improve CI/CD and deployment automation across services and environments
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.
Own and improve observability : monitoring, alerting, logging, dashboards, and actionable runbooks
Participate in on-call rotations and incident response; drive high-quality postmortems and follow-through
Partner closely with developers to improve system design, performance, and operational maturity
Strengthen engineering quality via PR reviews , shared standards, and knowledge sharing

Required qualifications, capabilities, and skills

A. Formal training or certification on software engineering concepts and 5+ years applied experience
Hands-on practical experience on AWS: ECS, EKS, Lambda, API Gateway, S3, DynamoDB, IAM, VPC, CloudWatch, KMS
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
Proficient in Terraform (modules, state management, workspaces)
Advanced understanding of Infrastructure as Code best practices (strong IaC and CI/CD patterns)
Good hands on Networking fundamentals: load balancers, DNS, firewalls, VPNs , Jenkins (pipelines, Groovy, shared libraries), Spinnaker (pipelines, deployment strategies, canary/blue-green), GitOps workflows, Containers and orchestration: Docker, Kubernetes/EKS
Artifact management: Nexus, ECR, JFrog
Experience with R eliability, Observability, Security, Compliance, Scripting and automation using Python, Bash, Go , JSON and Restful API

Preferred qualifications, capabilities, and skills