The Role

We are seeking a highly proactive Staff Software Engineer to drive the architecture of our next-generation validation guardrails and spearhead our AI-native developer experience across the organization. This role focuses on architecting engineering workflows across all traditional SDLC stages - Planning, Design, Build, Test, Deploy, and Maintain - into automated, closed-loop system where AI agents and committed markdown artifacts are able to accelerate delivery while keeping human engineers and product managers firmly in control of high-judgement decisions.

As a technical thought leader, you will help lead the design of intelligent frameworks, AI SDLC playbooks, and deterministic guardrails required to modernize our SDLC. Operating with a high degree of autonomy, you will lead grassroots efforts across engineering teams, navigate technical ambiguitity to advocate for best practices, mentor devleopers and engineer the friction-free internal platform for the future.

Applicants must be legally authorized to work in the United States for any employer without requiring current or future visa sponsorship (for example, employment-based visas such as H-1B, F-1/OPT, or similar), and must be authorized to begin work in the U.S. on their first day of employment.

What you’ll do

Technical Leadership & Grassroots Evangelism

• Adoption & Mentorship: Drive bottom-up adoption of AI-native development workflows across product teams by coaching engineers to transition into orchestration of parallel agentic sessions and subagents.
• Cross-Team Alignment: Partner with platform engineers, R&D teams, and security to dismantle traditional handoffs and convert manual sign-off cycles into automated artifact triggers with clear governance boundaries.

AI-Native Lifecycle Architecture

• Artifact-driven workflows: Operationalize markdown-driven artifact piplines to create transparent audit trails from idea to production.
• Deterministic Guardrails & Hooks: Architect deterministic build-time and deployment hooks to serve as safety boundaries behind AI development.
• Automated Code Review & PR pipelines: Integrate AI into PR review loop using structured policies and CI runners to enable agents to conduct multi-pass reviews and autonomously resolve comment threads.

Continuous Evals, Testing & Closed-Loop Operations

• Feedback loops & verifications: Design deterministic verification harnesses and visual feedback loops that allow AI agents to self-test, iterate on defect discoveries, and verify correctness before going for human review.
• Continuous Eval Infrastructure: Build continuous evaluation suites in CI to regression-test AI streering configs against sets of real engineerings tasks for whenever prompt or models change.
• Autonomous closed-loop maintenance: Implement automated production monitoring scripts to detect control breaches (failure spikes, latency drift) and design triage queues that feed back in for improvements to close the SDLC loop.

Who You Are

• Architectural Mastery: Deep conceptual and hands-on understanding of integrating AI into the development lifecycle across the entire SDLC.
• Data-Driven Core: A mindset that completely rejects gut feelings as the driving factor. You have demonstrated success moving the needle on DORA metrics and framework reliability using explicit data analysis.
• High Autonomy & Proactivity: Thrives in highly ambiguous environments. You don't wait for a ticket to be assigned; you actively detect friction, build the business case, and engineer the solution.
• Matured Engineering Skills: Strong programming skills in languages such as Python, Java, or JavaScript, with an emphasis on writing clean, maintainable, production-grade tools.
• Modern CI/CD Expertise: Deep hands-on experience with advanced CI/CD orchestration (e.g., GitHub Actions, Jenkins), infrastructure-as-code (Terraform), and containerization (Docker, Kubernetes).
• [Preferred] Experience managing complex software states and mimicking/isolating environments utilizing tools at enterprise scale.
• [Preferred] Past experience building custom internal wrapper applications or developer plugins around Large Language Models (LLMs).
• [Preferred] Proven success technically leading or closely pairing across split North American and Indian engineering hubs.
• [Preferred]Familiarity with service integration testing, contract testing tools, and developer catalog platforms (e.g., Backstage).