Lead Software Engineer - Java or Python, Agentic AI

JPMorgan Chase & Co. • Jersey City, NJ

2d • $156,750 - $215,000

About The Position

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

You will implement and operate evaluation and provenance subsystems under senior design direction, integrating agent-driven remediation workflows into enterprise delivery pipelines. You will help ensure these systems are measurable, observable, auditable, reliable, and ready for production operations. You will also support standards and tooling rollouts and promote safe, effective adoption of enterprise-authorized AI-assisted engineering practices.

JPMorgan Chase offers a competitive total rewards package, with base salary determined by role, experience, skill set, and location. Eligible roles may receive commission-based pay and/or discretionary incentive compensation. Benefits and programs are eligibility-dependent and include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, and financial coaching. The company is an equal opportunity employer and provides reasonable accommodations for religious practices and beliefs, and mental health or physical disability needs.

Requirements

• Formal training or certification in software engineering concepts and at least 5 years of applied experience developing, debugging, and maintaining code in a corporate environment using modern programming, scripting, and database-query languages.
• 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.
• Strong hands-on experience with Java, Spring Boot, Kafka, API development, Python, and Shell scripting for automation services, test harnesses, evaluation tooling, pipeline instrumentation, and operational tooling.
• Hands-on experience using enterprise-authorized AI-assisted software development tools for coding, test creation, troubleshooting, or documentation, and ability to critically evaluate and refine AI-generated output for correctness, performance, and security.
• Understanding of responsible AI use in engineering workflows, including data sensitivity, secure input and output handling, resiliency and security expectations, and the ability to guide peers on safe usage and controls.
• Hands-on experience with AWS or Azure, Kubernetes-based deployment automation, Docker, Helm, SQL databases, artifact repositories, secrets management, and controlled enterprise deployment environments.
• Comfort operating a subsystem end to end under senior design direction, including implementation, deployment, observability, alert response, production support, runbook improvement, and iteration based on measured quality and reliability signals.
• Working understanding of Git internals, branching workflows, PR and merge tooling, repository governance, commit signing, and large-scale change orchestration.
• Demonstrated experience leading effective use of approved AI-assisted software development tools and setting team expectations for validating outputs for correctness, performance, and security.
• Strong understanding of responsible AI practices, data sensitivity, secure handling of inputs and outputs, and resiliency and security expectations; experience coaching engineers on safe, compliant adoption.

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, or Scikit-learn.
• Experience automating infrastructure-as-code development or remediation using AI/ML-assisted workflows, including Terraform, Ansible, CloudFormation, or equivalent frameworks.
• Deep experience with observability automation and reliability analysis using Prometheus, Grafana, ELK/OpenSearch, OpenTelemetry, or equivalent 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 triggers, build and test gates, deployment-readiness checks, failure handling, and evidence capture for audit and operational review.
• Build and operate evaluation harnesses that measure agent quality and delivery readiness at scale, including success, regression, PR-merge, and pipeline pass rates, drift detection, and control effectiveness.
• Implement the PR-provenance contract under the Sr Lead's design, covering branch creation, CI/CD hooks, build-verification gates, automated test evidence, audit trails, signed commit and merge attestations, rollback envelopes, and operational handoffs for failed or blocked runs.
• Own harness subsystems such as evaluation-fixture management, replay tooling, regression corpora, quality-signal aggregation, failure triage, runbook maintenance, and day-to-day operational support.
• Co-own agent-harness reliability and observability through metrics, logs, traces, replay tooling, alerting, dashboards, and failure-pattern analysis.
• Contribute to agent-skill design reviews as an SME-capable reviewer of evaluation coverage and provenance implications, escalating audit-control questions to the L5.
• Support the Standards and Tooling pillars by integrating lint rules, template upgrades, quality gates, and migration checks into the evaluation harness and CI/CD flow for at-scale rollout.
• Instrument value, adoption, and operational metrics for evaluation and provenance subsystems, including repositories evaluated, provenance-signed PRs, pipeline pass and failure rates, repeat-run reduction, engineering days saved, and audit-evidence completeness.
• Drive team adoption of enterprise-authorized AI-assisted engineering practices, establishing consistent validation standards for secure coding, peer review, and automated testing and promoting effective patterns.
• Apply knowledge of the Software Development Life Cycle toolchain, including authorized AI-assisted development and automation capabilities, to improve the value realized from automation.

Benefits

• Comprehensive health care coverage
• On-site health and wellness centers
• Retirement savings plan
• Backup childcare
• Tuition reimbursement
• Mental health support
• Financial coaching
• Eligible roles may receive commission-based pay and/or discretionary incentive compensation in cash and/or forfeitable equity

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed