Lead Software Engineer - Java or Python, Agentic AI

JPMorgan Chase & Co.•Jersey City, NJ

1d•$156,750 - $215,000

About The Position

As a Lead Software Engineer at JPMorgan Chase within the Commercial & Investment Bank Digital Channel's team, you will build autonomous agent capabilities that can plan, execute, validate, and submit code changes in bulk. The role focuses on the evaluation harnesses, PR-provenance controls, CI/CD integrations, and operational readiness needed to scale machine-authored changes safely across runtime upgrades, framework migrations, security remediation, and standards adoption.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience of developing, debugging, and maintaining code in a corporate environment using modern programming, scripting, and database querying languages, such as Java, Python, Shell scripting, SQL, PostgreSQL, Oracle, SQL Server, or MySQL
Experience contributing to a CI/CD, DevOps, or release-engineering platform, including release pipelines, build and test automation, signed-attestation systems, deployment controls, or audit-trail tooling using platforms such as Jenkins, GitHub Actions, GitLab CI/CD, Bitbucket Pipelines, Harness, Argo CD, Artifactory or Nexus.
Strong hands-on Java, Spring Boot, Kafka, API development, Python, and Shell scripting experience for building automation services, test harnesses, evaluation tooling, pipeline instrumentation, and operational tooling using frameworks and tools such as REST APIs, OpenAPI/Swagger, Maven, Gradle, JUnit, Mockito, Selenium, Playwright, Cucumber, pytest, or SonarQube.
Hands-on experience using enterprise-authorized AI-assisted software development tools for coding, test creation, troubleshooting, or documentation, such as GitHub Copilot, Claude Code, enterprise-approved coding assistants, internal AI agents, with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, resiliency and security expectations, and the ability to guide peers on safe and effective usage within team practices using responsible AI controls, secure prompt and input-handling practices, AI output validation checklists, model-output review workflows, and audit evidence capture.
Hands-on experience with AWS or Azure cloud platforms, Kubernetes-based deployment automation, Docker, Helm, SQL databases, artifact repositories, secrets management, and controlled enterprise deployment environments using tools such as HashiCorp Vault, AWS Secrets Manager, Azure Key Vault, container registries, blue/green deployment, canary releases, rollback automation, release gates, or environment promotion workflows.
Comfort operating end-to-end on a subsystem under senior design direction, including implementation, deployment, observability, alert response, production support, runbook improvement, and iteration on measured quality and reliability signals using observability and reliability tools such as Prometheus, Grafana, Splunk, ELK/OpenSearch, OpenTelemetry, Datadog, or AppDynamics.
Working understanding of Git internals, branching workflows, PR and merge tooling, repository governance, commit signing, and large-scale change orchestration using tools and controls such as Git, Bitbucket, GitHub, GitLab, branch protections, signed commits, PR approval workflows, Dependabot-style dependency scanning, Checkmarx, Snyk, Black Duck, or Fortify.
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

Nice To Haves

Formal training or certification in software engineering, AI/ML engineering, or cloud-native engineering, with experience applying agent-based systems or automation in enterprise delivery.
Proficiency in Python, Java, or similar languages, with exposure to model-evaluation tooling or ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or equivalent platforms.
Experience automating infrastructure-as-code development or remediation using AI/ML-assisted workflows, including Terraform, Ansible, CloudFormation, or equivalent enterprise IaC frameworks.
Deep experience with observability automation and reliability analysis using Prometheus, Grafana, ELK/OpenSearch, OpenTelemetry, or equivalent enterprise monitoring platforms.
Excellent problem-solving, communication, and collaboration skills, with experience partnering across engineering, security, platform, SRE, and application-owner teams.

Responsibilities

Design and integrate AI-driven remediation workflows into enterprise CI/CD pipelines, including trigger design, build and test gating, deployment readiness checks, failure handling, and evidence capture for downstream audit and operational review.
Build and operate the evaluation harness that proves agent quality and delivery readiness at scale, including success rate, regression rate, PR-merge rate, pipeline pass rate, drift detection, and control effectiveness across runtime, framework, standards, and CVE-remediation skills.
Implement the PR-provenance contract end-to-end under the Sr Lead's design, including branch creation, CI/CD hook integration, build-verification gates, automated test evidence, audit-trail emission, signed commit and merge attestation, rollback envelope, and operational handoff for failed or blocked runs.
Own specific subsystems within the harness, including evaluation-fixture management, replay tooling, regression corpora, quality-signal aggregation, failure triage, runbook maintenance, and day-to-day operational support.
Co-own the agent harness's reliability and observability including metrics, logs, traces, replay tooling, alerting, dashboards, and failure-pattern analysis so agent behavior and pipeline outcomes are diagnosable at scale.
Contribute to agent-skill design reviews as an SME-capable second pair of eyes on eval-coverage and provenance implications; escalate audit-control questions to the L5.
Support the Standards pillar and Tooling pillar by wiring at-scale rollout of new lint rules, template upgrades, quality gates, and migration checks into the evaluation harness and CI/CD flow so bulk agent runs can prove standards adoption at Channels scope.
Instrument value, adoption, and operational metrics for the evaluation and provenance subsystems, including number of repositories evaluated, number of PRs provenance-signed, pipeline pass and failure rates, repeat-run reduction, engineering days saved, and audit-evidence completeness.
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.