Senior Lead Software Engineer, Agentic AI

What you'll do

• Build autonomous agent capabilities to plan, execute, and submit code changes in bulk for large-scale migrations and remediations.
• Integrate AI-driven remediation workflows into enterprise CI/CD pipelines including gating, deployment checks, and audit evidence capture.
• Develop and operate evaluation harnesses to measure agent quality, success rates, and performance across various technical domains.
• Implement end-to-end PR-provenance contracts including branch creation, signed commits, and automated verification gates.
• Manage subsystems for the evaluation harness such as replay tooling, regression corpora, and failure triage.
• Ensure reliability and observability of agent systems through metrics, logs, tracing, and proactive alerting.
• Instrument and report on key performance indicators regarding AI adoption, engineering hours saved, and audit completeness.
• Drive team adoption of enterprise-authorized AI tools while enforcing standards for secure coding and automated testing.

What we're looking for

• Formal training or certification in software engineering and 5+ years of experience developing, debugging, and maintaining code in a corporate environment.
• Experience contributing to CI/CD, DevOps, or release-engineering platforms including build automation, deployment controls, and audit-trail tooling.
• Hands-on experience with Java, Spring Boot, Kafka, API development, Python, and Shell scripting for building automation services and test harnesses.
• Hands-on experience using enterprise-authorized AI-assisted software development tools to create code, tests, and documentation while validating outputs for security.
• Understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs/outputs, and guiding peers on safe usage.
• Experience with AWS or Azure cloud platforms, Kubernetes, Docker, Helm, and various SQL databases and secret management tools.
• Proficiency in Git internals, branching workflows, repository governance, and large-scale change orchestration.
• Experience automating infrastructure-as-code using AI/ML-assisted workflows (preferred); experience with ML frameworks like TensorFlow or PyTorch (preferred).