About the role
About Enterpret As the world moves to AI native applications, one element matters more than anything: the data. At Enterpret, we are unlocking the most impactful dataset for businesses, customer feedback. We centralize feedback across every source, like Twitter, Salesforce, Zendesk, Slack, Intercom, community forums, surveys, and Gong calls, into a single knowledge graph. On top of it we build an adaptive taxonomy for each customer and an agent platform that lets teams query, monitor, and act on customer intelligence wherever they work, whether that's inside Enterpret, in Slack, or in their own AI tools through our MCP server. Our customers include companies like Canva, Notion, WisprFlow, ElevenLabs, OpenRouter, Western Union, and several other Product Led Growth decacorns and F500 companies.We are backed by Kleiner Perkins, Sequoia Capital India, and Unusual Ventures. Our engineering culture is AI-native from the ground up. Agents, LLMs, and applied ML are not a side project here, they are the core of how we build. We work at the edge of what is possible in agentic systems and push past it. Read more about our team, core values, and operating principles - here . What You Will Do Own architecture for Wisdom and Agents (our agent-driven engine that reasons over unstructured customer feedback and turns it into structured product insight), alongside Applications and our Context/Contact Graph. This is production agent and ML infrastructure, not a lab experiment. Convert product direction into phased technical plans: estimates, milestones, and risk calls included. Make trade-offs across correctness, latency, UX, AI/model behavior, reliability, cost, and delivery speed , and be able to defend them. Define clean responsibility boundaries across frontend, BFF, backend services, agent workflows, ML platforms, and data contracts. Review ERDs, PRDs, design docs, and implementation approaches, catching missing technical or product details before they turn into execution churn. Drive unblock planning on your areas while keeping ownership with area leads and senior engineers, not stepping in to run their work for them. Partner tightly with Product, Design, Engineering, and GTM so complex problem statements become clear and executable, and contribute directly to product direction. Mentor and influence senior engineers without relying on reporting authority, while staying hands-on enough to review code, reason through incidents, and catch implementation risk yourself. What It Takes 8+ years of experience as a Staff+/Principal-level engineer, architect, or technical lead, or equivalent demonstrated depth. Track record owning architecture and execution for multi-team product or platform work, not just contributing to it. Strong foundation in distributed systems , with working knowledge across agent systems, ML engineering, ML platforms/MLOps, and frontend/BFF architecture . This role is not backend-only, and needs enough range to own end-to-end product problems with confidence. Proven ability to turn ambiguity into executable engineering plans, backed by strong written communication in design docs, trade-off memos, and RCA-style reasoning. A track record of mentoring and influencing engineers without relying on formal authority. Comfort challenging