Senior Lead Software Engineer, Agentic AI

JPMorgan Chase

Confirmed live today Trusted

Quick summary

Work type: On-site
Location: Jersey City, NJ
Employment: Full-time
Posted: 1 day ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1260 open roles on FindRole.

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At a glance

TL;DR · Senior Lead Software Engineer, Agentic AI

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As a Senior Lead Software Engineer within the Commercial & Investment Bank Digital Channel's team, you will build autonomous agent capabilities to plan, execute, and submit code changes in bulk. You will design and integrate AI-driven remediation workflows into enterprise CI/CD pipelines, building evaluation harnesses to prove agent quality and delivery readiness at scale. Your daily work involves implementing PR-provenance contracts, managing evaluation-fixture systems, and ensuring reliability through observability. You will utilize Java, Python, Spring Boot, Kafka, SQL, and Shell scripting alongside tools like Jenkins, GitHub Actions, Docker, Kubernetes, and AWS or Azure. The role solves the technical challenge of scaling machine-authored changes safely across runtime upgrades, framework migrations, and security remediations while ensuring responsible AI use, audit-trail emission, and consistent validation standards across the enterprise.

Skills

Java Python SQL Shell scripting Spring Boot Kafka PostgreSQL Oracle SQL Server MySQL Jenkins GitHub Actions GitLab CI/CD Bitbucket Pipelines Harness Argo CD Artifactory Nexus REST APIs OpenAPI Swagger Maven Gradle JUnit Mockito Selenium Playwright Cucumber pytest SonarQube GitHub Copilot AWS Azure Kubernetes Docker Helm HashiCorp Vault AWS Secrets Manager Azure Key Vault Prometheus Grafana Splunk ELK OpenSearch OpenTelemetry Datadog AppDynamics Git Terraform Ansible CloudFormation TensorFlow PyTorch Scikit-learn CI/CD DevOps infrastructure-as-code

What you'll do

Design and integrate AI-driven remediation workflows into enterprise CI/CD pipelines with build gating and audit evidence capture.
Build and operate an evaluation harness to prove agent quality, success rates, and drift detection at scale.
Implement end-to-end PR-provenance contracts including branch creation, signed commit attestations, and automated rollback envelopes.
Own specific subsystems including evaluation-fixture management, replay tooling, regression corpora, and failure triage.
Co-own agent harness reliability by managing metrics, logs, traces, alerting, and failure-pattern analysis.
Wire at-scale rollouts of lint rules, template upgrades, and migration checks into the evaluation harness.
Instrument value and operational metrics for evaluation subsystems, including engineering days saved and audit-evidence completeness.
Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality and delivery speed.

What we're looking for

Formal training or certification in software engineering and 5+ years of experience developing, debugging, and maintaining code in a corporate environment.
Proficiency in Java, Spring Boot, Kafka, API development, Python, and Shell scripting for building automation services and test harnesses.
Experience contributing to CI/CD, DevOps, or release-engineering platforms using tools like Jenkins, GitHub Actions, GitLab CI/CD, or Argo CD.
Hands-on experience using enterprise-authorized AI-assisted software development tools to create, troubleshoot, and validate AI-generated outputs.
Understanding of responsible AI use, including data sensitivity, secure input/output handling, and guiding peers on safe engineering practices.
Experience with AWS or Azure cloud platforms, Kubernetes, Docker, Helm, and secrets management tools like HashiCorp Vault or AWS Secrets Manager.
Ability to operate end-to-end on subsystems including deployment, observability, alert response, and production support using tools like Prometheus or Splunk.
Working understanding of Git internals, branching workflows, repository governance, and large-scale change orchestration.

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