AI Product Engineer

About the Role

Role Overview We are transitioning our organization to an AI-first operational model. We are seeking an AI Product Engineer to architect and build the company’s central intelligence engine. In this role, you will be the primary builder of automated intelligence systems—tools that not only retrieve insights but actively trigger workflows, and synthesize strategic intel across our entire business landscape. Role Profile This is a high-impact, hands-on engineering role requiring a convergence of three distinct skill sets: Software Engineering (50%): Building scalable middleware, API integrations, and production-grade applications that connect our data stack to business tools (CRM, Marketing Automation, Support/Ticketing). AI Engineering (30%): Implementing Agentic workflows, RAG architectures, and LLMs to process unstructured data at scale. Data Analytics (20%): Leveraging SQL and B2B SaaS metrics to ensure all automation is grounded in accurate, governed data. Key Responsibilities 1. Building the "Enterprise Brain" (Architecture & Integration) Develop a unified intelligence layer that ingests signals from disparate sources (Product Telemetry, CRM, Call Transcripts, Marketing inputs) and processes them into actionable outputs. Build robust integrations/webhooks to push AI-generated insights directly into workflow tools (e.g., pushing "Churn Risk" alerts into Salesforce 2. AI Logic & Agent Implementation Architect "Agentic" workflows where LLMs are granted permission to perform tasks  Implement advanced RAG to ground AI outputs in company documentation, historical data, and strategic context. Ensure rigorous evaluation and guardrails for non-deterministic models to prevent "hallucinations" in critical business workflows. 3. Data Engineering & Governance Collaborate with Analytics Engineers to ensure the underlying data pipelines support real-time or near-real-time AI applications. Maintain security and privacy standards, ensuring that AI agents respect data access permi

Key Responsibilities

• 1. Building the "Enterprise Brain" (Architecture & Integration)
• 2. AI Logic & Agent Implementation
• 3. Data Engineering & Governance

Requirements & Qualifications

• Strong problem solving foundations, knowledge of modern engineering practices, and interest in technology systems.

Perks & Benefits

• Official mentor support
• verified hiring timeline
• and career progression.