Senior Lead Software Engineer - Java or Python, Agentic AI (Commercial & Investment Bank)
Senior lead engineer on JPMorgan Chase's Commercial & Investment Bank Digital Channel team building autonomous AI agents that plan, execute, and submit bulk code changes. The work centers on evaluation harnesses, PR-provenance controls, and CI/CD integrations using Java/Python, Spring Boot, Kafka, Jenkins/GitHub Actions, Kubernetes, and AWS/Azure.
Compensation
Not disclosed
Location
Jersey City, United States
Work setup
Not specified
Seniority
Lead
Employment
Contract
Experience
5+ years
Category
Software engineering
Posted
Skills
agentic-ai
ai
ansible
api
appdynamics
argocd
artifactory
automation
aws
azure
bash
bitbucket
ci-cd
claude-code
cloud
cloud-native
cloudformation
cucumber
datadog
devops
docker
elk
git
github
github-actions
github-copilot
gitlab
gradle
grafana
helm
infrastructure-as-code
java
jenkins
junit
kafka
kubernetes
machine-learning
maven
mockito
model-evaluation
mysql
observability
openapi
opensearch
opentelemetry
oracle
playwright
postgresql
prometheus
pytest
python
pytorch
rest
root-cause-analysis
scikit-learn
secrets-management
secure-coding
selenium
snyk
sonarqube
splunk
spring
sql
sql-server
sre
swagger
tensorflow
terraform
test-automation
vault
Must have
Formal training or certification on software engineering concepts and 5+ years applied experience developing, debugging, and maintaining code in a corporate environment using languages such as Java, P
Experience contributing to a CI/CD, DevOps, or release-engineering platform (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, and pipeline instrumentation
Hands-on experience with enterprise-authorized AI-assisted development tools (GitHub Copilot, Claude Code, internal AI agents) with ability to validate and refine AI-generated outputs
Understanding of responsible AI use in engineering workflows, including data sensitivity, secure input/output handling, and audit evidence capture
Hands-on experience with AWS or Azure, Kubernetes, Docker, Helm, SQL databases, secrets management, and controlled enterprise deployment workflows
Comfort operating end-to-end on a subsystem including deployment, observability, alert response, and production support using tools like Prometheus, Grafana, Splunk, or OpenTelemetry
Working understanding of Git internals, branching workflows, PR/merge tooling, repository governance, commit signing, and large-scale change orchestration
Demonstrated experience leading effective use of AI-assisted development tools and setting team expectations for validating AI outputs
Nice to have
Formal training or certification in software engineering, AI/ML engineering, or cloud-native engineering, with experience applying agent-based systems in enterprise delivery
Proficiency in Python or Java with exposure to model-evaluation tooling or ML frameworks (TensorFlow, PyTorch, Scikit-learn)
Experience automating infrastructure-as-code development or remediation with AI/ML-assisted workflows (Terraform, Ansible, CloudFormation)
Deep experience with observability automation and reliability analysis (Prometheus, Grafana, ELK/OpenSearch, OpenTelemetry)
Excellent problem-solving, communication, and collaboration skills with cross-team partnering experience
Full description
hackajob is collaborating with J.P. Morgan to connect them with exceptional professionals for this role.
JOB DESCRIPTION
As a Senior 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.
Job 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.
Senior Lead Software Engineer - Java or Python, Agentic AI (Commercial & Investment Bank) at J.P. Morgan (Jersey City, United States) — aijobsearch
Strong understanding of responsible AI practices with experience coaching engineers on safe, compliant adoption
- 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.
Required Qualifications, Capabilities, and Skills
- 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
Preferred Qualifications, Capabilities, and Skills
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.
ABOUT US
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans
ABOUT THE TEAM
J.P. Morgan's Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.