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About this 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 requirements constructively across Product, Design, Engineering, and GTM. High agency, low ego, strong attention to detail.
What Success Looks Like
- In 3 months: You have taken full ownership of architecture and execution for one complex, ambiguous area within Core Product.
- In 6 months: You are independently driving one or two areas end to end, technical direction through execution health, and Product and senior engineers trust your calls.
- In 12 months: You have raised the technical bar for Core Product, creating standards for architecture review, estimation, and production readiness that outlast your own projects.
Why Enterpret?
- High Impact: Architect at the frontier of agentic, AI-native product engineering: the agent workflows, ML platforms, and reasoning systems that decide how Enterpret turns raw customer feedback into product intelligence, with your fingerprints on the systems the rest of engineering builds around.
- Ownership: End-to-end responsibility for features and systems.
- Complex Challenges: Work where agent reasoning, ML platforms, and product architecture collide, the kind of problems most engineers only get to read papers about, here shipped to real customers like Canva and Notion.
- Growth: Get hands-on, production-scale depth in agent systems and ML platforms that is hard to find anywhere else, whether you are already an AI specialist looking for real ownership, or a strong distributed-systems engineer looking to go all-in on AI-native architecture.
- Culture: Open, collaborative, and values-driven environment with autonomy.
- Benefits: Competitive salary, equity, hybrid work setup, premium healthcare, and more.
What We Value
At Enterpret, we operate with a deep sense of ownership. We play for the team and do what it takes to win together. We care personally for our teammates while pushing each other with honest, actionable feedback. Above all, we approach everything with humility and a drive to keep learning and getting better.
Equal Opportunities
We are an equal opportunity employer. We ensure that none of our employees or prospective employees receives less favourable treatment as a result of age, sex, disability, marital status, colour, race, religion or ethnic origin. Equally we aim to ensure that no such employee is disadvantaged by terms and conditions of employment which cannot be justified.