Lead AI Engineer, Enterprise AI Operations

Role Overview

• Serve as the primary AI engineering partner to the CEO and executive leadership team, translating ideas, strategic questions, and emerging concepts into production-ready AI agents and workflows with minimal oversight.
• Independently take ideas from concept to production, shaping problem statements, designing system architecture, implementing code, validating outputs, and operationalizing solutions without requiring heavy product or engineering management.
• Design and implement complex, multi-step agentic workflows, including multi-agent orchestration, retrieval-augmented generation (RAG), tool use, memory strategies, evaluation frameworks, and cross-system automation.
• Develop production-grade AI systems using modern LLMs, orchestration frameworks, and internal tooling, with strong attention to scalability, performance, observability, and clean engineering practices.
• Operationalize AI responsibly by implementing guardrails, structured evaluations, monitoring, and validation layers to ensure predictable behavior, reliability, and compliance.
• Partner closely with Security and Legal to properly gate sensitive use cases, implementing access controls, audit logging, data minimization, and enterprise-grade governance patterns while balancing safety and speed.
• Translate ambiguous, high-visibility problems into clear technical solutions, balancing speed, quality, and risk while maintaining a high bar for accuracy and trust.
• Evaluate, select, and rationalize AI tools and platforms, contributing to capability-to-tool decisions and ensuring consolidation, security alignment, and long-term sustainability across the enterprise.
• Support post-launch adoption and iteration, incorporating feedback, refining workflows, and continuously improving performance, usability, and measurable impact.
• Contribute to org-wide AI maturity by documenting architectural patterns, sharing best practices, and establishing repeatable approaches that elevate internal AI capabilities across Webflow.

Requirements

• 7-10+ years of professional software engineering experience designing, building, and operating complex production systems in cloud environments or equivalent practical experience as outlined.
• Proven experience building and deploying AI-powered systems in production, including agent-based or multi-step workflows (RAG, orchestration, tool-calling, memory strategies, evaluation, and failure handling).
• Strong proficiency in modern programming languages (e.g., Python, TypeScript) with demonstrated ability to write clean, maintainable, production-quality code.
• Deep engineering discipline across clean architecture, distributed systems, APIs, CI/CD, testing strategies, and production observability.
• Experience partnering directly with senior or executive stakeholders, translating ambiguous ideas into scalable, technically sound solutions with measurable impact across enterprise systems.

Tech Stack

• Cloud
• Distributed Systems
• Python
• TypeScript

Benefits

• Ownership in what you help build.
• Health coverage that actually covers you.
• Support for every stage of family life.
• Time off that’s actually off.
• Wellness for the whole you.
• Invest in your future.
• Monthly stipends that flex with your life.
• Bonus for building together.