Lead Software Engineer - Java or Python, Agentic AI
On-siteJersey City, New Jersey, United States
Jersey City, New Jersey, United StatesOn-siteFull TimeSenior LevelFinancial ServicesEnterprise
Full TimeSenior LevelEnterpriseFinancial Services
Job Summary
Design and integrate AI-driven remediation workflows into enterprise CI/CD pipelines, including trigger design, build gating, and audit evidence capture. Build and operate evaluation harnesses to prove agent quality and delivery readiness at scale, managing success rates, regression metrics, and drift detection across runtime and framework migrations. Implement end-to-end PR-provenance contracts featuring branch creation, automated test evidence, signed commit attestation, and operational handoff for failed runs. Own subsystems for evaluation-fixture management, replay tooling, and failure triage while driving team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality and delivery speed.
Required Qualifications
- Formal training or certification on software engineering concepts
- 5+ years applied experience of developing, debugging, and maintaining code in a corporate environment
- Experience 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
- Experience using platforms such as Jenkins, GitHub Actions, GitLab CI/CD, Bitbucket Pipelines, Harness, Argo CD, Artifactory or Nexus
- Hands-on Java experience
- Hands-on Spring Boot experience
- Hands-on Kafka experience
- Hands-on API development experience
- Hands-on Python experience
- Hands-on Shell scripting experience
- Experience building automation services, test harnesses, evaluation tooling, pipeline instrumentation, and operational tooling
- Experience 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
- Experience with tools such as GitHub Copilot, Claude Code, enterprise-approved coding assistants, internal AI agents
- Demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security
- Understanding of responsible AI use in engineering workflows
- Understanding of data sensitivity
- Understanding of secure handling of inputs and outputs
- Understanding of resiliency and security expectations
- Ability to guide peers on safe and effective usage within team practices
- Experience using responsible AI controls
- Experience using secure prompt and input-handling practices
- Experience using AI output validation checklists
- Experience using model-output review workflows
- Experience using audit evidence capture
- Hands-on experience with AWS or Azure cloud platforms
- Hands-on experience with Kubernetes-based deployment automation
- Hands-on experience with Docker
- Hands-on experience with Helm
- Hands-on experience with SQL databases
- Hands-on experience with artifact repositories
- Hands-on experience with secrets management
- Hands-on experience with controlled enterprise deployment environments
- Experience 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
- Experience with implementation, deployment, observability, alert response, production support, runbook improvement, and iteration on measured quality and reliability signals
- Experience using observability and reliability tools such as Prometheus, Grafana, Splunk, ELK/OpenSearch, OpenTelemetry, Datadog, or AppDynamics
- Working understanding of Git internals
- Working understanding of branching workflows
- Working understanding of PR and merge tooling
- Working understanding of repository governance
- Working understanding of commit signing
- Working understanding of large-scale change orchestration
- Experience 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
- Ability to set team expectations for validating AI outputs for correctness, performance, and security
- Strong understanding of responsible AI use in engineering workflows
- Experience coaching engineers on safe, compliant adoption within delivery practices
Desired Qualifications
- Formal training or certification in software engineering, AI/ML engineering, or cloud-native engineering
- Experience applying agent-based systems or automation in enterprise delivery
- Proficiency in Python, Java, or similar languages
- 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
- Experience using 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
- Experience partnering across engineering, security, platform, SRE, and application-owner teams
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