Lead Software Engineer, Agentic AI

JPMorgan Chase

Confirmed live today High trust

Quick summary

Work type: On-site
Location: Jersey City, NJ
Employment: Full-time
Posted: 18 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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Similar $199k

$137k $253k

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Based on 240 similar postings.

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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 · Lead Software Engineer, Agentic AI

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Lead Software Engineer - Java or Python, Agentic AI joins the Commercial & Investment Bank Digital Channel's team to build autonomous agent capabilities for bulk code changes. You will design and integrate AI-driven remediation workflows into enterprise CI/CD pipelines, building evaluation harnesses to prove agent quality, success rates, and drift detection. Responsibilities include implementing PR-provenance contracts, managing evaluation-fixture systems, and owning reliability metrics using logs, traces, and dashboards. 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 addresses the technical challenge of scaling machine-authored changes safely across runtime upgrades, framework migrations, and security remediations while ensuring responsible AI use, secure prompt handling, and audit-trail emission within a corporate software development life cycle.

Skills

Java Python SQL Spring Boot Kafka PostgreSQL Oracle SQL Server MySQL CI/CD 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 AWS Azure Kubernetes Docker Helm HashiCorp Vault Prometheus Grafana Splunk ELK OpenSearch OpenTelemetry Datadog AppDynamics Git Terraform Ansible CloudFormation TensorFlow PyTorch Scikit-learn

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 measure agent quality, success rates, and regression metrics at scale.
Implement end-to-end PR-provenance contracts including branch creation, signed commit attestations, and automated rollback envelopes.
Own specific subsystems for evaluation-fixture management, replay tooling, regression corpora, and failure triage.
Manage reliability and observability for the agent harness using metrics, logs, traces, and alerting dashboards.
Wire at-scale rollouts of lint rules, template upgrades, and quality gates into the CI/CD flow.
Instrument and monitor value metrics such as engineering days saved and audit-evidence completeness.
Drive team adoption of enterprise-authorized AI-assisted engineering practices and validation standards.

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.
Hands-on experience with AWS or Azure, Kubernetes, Docker, Helm, and secrets management tools like HashiCorp Vault or AWS Secrets Manager.
Experience operating subsystems including implementation, deployment, observability, and production support using tools like Prometheus, Grafana, or Splunk.
Working understanding of Git internals, branching workflows, commit signing, and repository governance using tools like GitHub or Bitbucket.

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