What You Will Do :

- Own architecture for agent-driven systems, alongside core applications and context/data platforms. This is production-grade agent and ML infrastructure, not a lab experiment.

- Convert product direction into phased technical plans, including estimates, milestones, dependencies, and risk calls.

- Make and defend trade-offs across correctness, latency, UX, AI/model behavior, reliability, cost, and delivery speed.

- Define clear responsibility boundaries across frontend, BFF, backend services, agent workflows, ML platforms, and data contracts.

- Review ERDs, PRDs, design docs, and implementation approaches, identifying missing technical or product details before they create execution churn.

- Drive technical unblocking across your areas while keeping ownership with area leads and senior engineers.

- Partner closely with Product, Design, Engineering, and GTM to turn complex problem statements into clear, executable solutions and contribute directly to product direction.

- Mentor and influence senior engineers without relying on reporting authority, while remaining hands-on enough to review code, reason through incidents, and identify implementation risks.

What It Takes :

- 8+ years of experience as a Staff+/Principal-level engineer, architect, or technical lead, or equivalent demonstrated depth.

- Track record of owning architecture and execution for multi-team product or platform initiatives, not just contributing to them.

- Strong foundation in distributed systems, with working knowledge across agent systems, ML engineering, ML platforms/MLOps, and frontend/BFF architecture.

- Ability to own end-to-end product problems with confidence; this is not a backend-only role.

- Proven ability to turn ambiguity into executable engineering plans, supported by strong written communication through design docs, trade-off memos, and RCA-style reasoning.

- Track record of mentoring and influencing engineers without relying on formal authority.

- Comfort constructively challenging requirements across Product, Design, Engineering, and GTM.

- High agency, low ego, and strong attention to detail.

What Success Looks Like :

- In 3 months: You have taken full ownership of architecture and execution for one complex, ambiguous product area.

- In 6 months: You are independently driving one or two areas end to end, from technical direction through execution health, with strong trust from Product and senior engineers.

- In 12 months: You have raised the technical bar across the product organization, establishing standards for architecture review, estimation, and production readiness that outlast your own projects.

Why This Role :

- High Impact: Architect AI-native product systems involving agent workflows, ML platforms, and reasoning systems that transform complex, unstructured data into actionable product intelligence.

- Ownership: Take end-to-end responsibility for major features, systems, and technical direction.

- Complex Challenges: Solve problems where agent reasoning, ML platforms, distributed systems, and product architecture intersectsystems that are shipped to real customers at scale.

- Growth: Build deep, production-scale expertise in agent systems and ML platforms, whether you're already an AI specialist seeking greater ownership or a distributed-systems engineer looking to move deeper into AI-native architecture.

- Culture: Open, collaborative, autonomous, and values-driven environment.

- Benefits: Competitive compensation, equity, hybrid work setup, premium healthcare, and more.