OVERVIEW The AI Software Development team is charged with transforming how our engineering organization designs, builds, and ships software. The team drives the adoption of AI-assisted tooling across the full software development lifecycle, deploys autonomous agents that expand engineering capacity, and ensures that every new generation of AI capability is evaluated, integrated, and operationalized with rigor.
s the AI SDLC Lead, you own the engineering organization's AI-enhanced developer experience end to end. You are responsible for selecting, integrating, and continuously evolving the toolchain that makes each engineer measurably more productive - spanning IDE intelligence, code review automation, documentation generation, and test augmentation. You set the technical direction for AI tooling across the SDLC and ensure those investments deliver compounding value rather than accumulate as technical debt.
KEY RESPONSIBILITIES - Own the AI developer tooling strategy, roadmap, and vendor relationships - from IDE assistants through automated code review, documentation, and test generation.
- rchitect tool integrations across the SDLC, ensuring AI tooling is embedded in CI/CD pipelines, code review workflows, and IDE environments at scale.
- Define and enforce tooling standards, integration patterns, and deprecation policies across the engineering organization.
- Lead technical evaluations of new tooling entrants and maintain a structured vendor scorecard tied to empirical productivity benchmarks.
- Partner with the InfoSec Engineer to ensure all tooling integrations comply with data handling, code confidentiality, and credential security requirements.
- Drive tooling telemetry design - instrumentation, data pipelines, and dashboards that surface productivity capture rates and regression indicators.
- Mentor and guide the AI Solutions Engineer and Champion Network on tooling best practices and integration patterns.
- Represent tooling strategy in CTO-level reviews and cross-functional planning sessions.
- Support tooling platform capability planning and cost modeling as AI tool adoption scales across the engineering organization.
REQUIRED QUALIFICATIONS - 7+ years of software engineering experience with depth in platform engineering, developer experience, or developer tooling.
- Track record of owning and scaling developer tooling platforms across 50+ engineer organizations.
- Deep hands-on experience with AI-assisted development tools - GitHub Copilot, Cursor, JetBrains AI, or equivalents.
- Direct experience with prompt engineering and LLM-based developer tools integrated into engineering workflows.
- Familiarity with AI coding capability benchmarks - including SWE-bench, METR autonomous task research, and similar empirical evaluation frameworks.
- Strong understanding of SDLC toolchains: CI/CD, SCM, code review, artifact management, and observability.
- Experience driving technical evaluations, RFPs, and vendor negotiations for engineering tooling.
- bility to design measurement and instrumentation systems for developer productivity.
- Strong cross-functional communication skills - capable of translating technical tooling decisions into business outcomes.
NICE TO HAVE - Experience integrating LLM-based tools into enterprise software delivery pipelines at a regulated or security-sensitive organization.
- Contributions to open-source developer tooling projects.
- Familiarity with IP and code confidentiality risk models in AI-assisted coding environments.
- Prior experience presenting technology strategy to CTO or CIO-level leadership.
