{"schemaVersion":"jobsearcher.job.v1","id":"c654bfd2ad77c3e1237f7883","url":"https://jobsearcher.com/jobs/c654bfd2ad77c3e1237f7883","canonicalUrl":"https://jobsearcher.com/jobs/c654bfd2ad77c3e1237f7883","title":"Senior Lead Software Engineer - Java or Python, Agentic AI","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.\r\nJob ResponsibilitiesDesign 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.\r\nBuild 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.\r\nImplement 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.\r\nOwn 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.\r\nCo‑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.\r\nContribute to agent‑skill design reviews as an SME-capable second pair of eyes on eval‑coverage and provenance implications; elevate audit‑control questions to the L5.\r\nSupport 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.\r\nInstrument 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.\r\nDrives 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.\r\nApplies 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.\r\nRequired Qualifications, Capabilities, and SkillsFormal 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\r\nExperience 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.\r\nStrong 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.\r\nHands‑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.\r\nUnderstanding 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.\r\nHands‑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.\r\nComfort 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.\r\nWorking 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.\r\nDemonstrated 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.\r\nStrong 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\r\nPreferred Qualifications, Capabilities, and SkillsFormal training or certification in software engineering, AI/ML engineering, or cloud‑native engineering, with experience applying agent‑based systems or automation in enterprise delivery.\r\nProficiency in Python, Java, or similar languages, with exposure to model‑evaluation tooling or ML frameworks such as TensorFlow, PyTorch, Scikit‑learn, or equivalent platforms.\r\nExperience automating infrastructure‑as‑code development or remediation using AI/ML‑assisted workflows, including Terraform, Ansible, CloudFormation, or equivalent enterprise IaC frameworks.\r\nDeep experience with observability automation and reliability analysis using Prometheus, Grafana, ELK/OpenSearch, OpenTelemetry, or equivalent enterprise monitoring platforms.\r\nExcellent problem‑solving, communication, and collaboration skills, with experience partnering across engineering, security, platform, SRE, and application‑owner teams.#J-18808-Ljbffr","company":"Kardow","rawCompany":"kardow","city":"Jersey City","state":"NJ","isRemote":false,"isActive":true,"createdAt":"2026-10-04T01:52:28.025Z","occupations":[{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1251.00","title":"Computer Programmers","slug":"computer-programmers"},{"code":"15-1253.00","title":"Software Quality Assurance Analysts and Testers","slug":"software-quality-assurance-analysts-and-testers"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Senior Lead Software Engineer - Java or Python, Agentic AI","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.\r\nJob ResponsibilitiesDesign 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.\r\nBuild 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.\r\nImplement 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.\r\nOwn 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.\r\nCo‑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.\r\nContribute to agent‑skill design reviews as an SME-capable second pair of eyes on eval‑coverage and provenance implications; elevate audit‑control questions to the L5.\r\nSupport 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.\r\nInstrument 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.\r\nDrives 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.\r\nApplies 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.\r\nRequired Qualifications, Capabilities, and SkillsFormal 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\r\nExperience 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.\r\nStrong 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.\r\nHands‑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.\r\nUnderstanding 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.\r\nHands‑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.\r\nComfort 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.\r\nWorking 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.\r\nDemonstrated 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.\r\nStrong 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\r\nPreferred Qualifications, Capabilities, and SkillsFormal training or certification in software engineering, AI/ML engineering, or cloud‑native engineering, with experience applying agent‑based systems or automation in enterprise delivery.\r\nProficiency in Python, Java, or similar languages, with exposure to model‑evaluation tooling or ML frameworks such as TensorFlow, PyTorch, Scikit‑learn, or equivalent platforms.\r\nExperience automating infrastructure‑as‑code development or remediation using AI/ML‑assisted workflows, including Terraform, Ansible, CloudFormation, or equivalent enterprise IaC frameworks.\r\nDeep experience with observability automation and reliability analysis using Prometheus, Grafana, ELK/OpenSearch, OpenTelemetry, or equivalent enterprise monitoring platforms.\r\nExcellent problem‑solving, communication, and collaboration skills, with experience partnering across engineering, security, platform, SRE, and application‑owner teams.#J-18808-Ljbffr","datePosted":"2026-10-04T01:52:28.025Z","dateModified":"2026-10-04T01:52:28.025Z","hiringOrganization":{"@type":"Organization","name":"Kardow","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Jersey City","addressRegion":"NJ","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"c654bfd2ad77c3e1237f7883"},"url":"https://jobsearcher.com/jobs/c654bfd2ad77c3e1237f7883"}}