AI Product Engineer

Job description

### **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: 1. **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). 2. **AI Engineering (30%):** Implementing Agentic workflows, RAG architectures, and LLMs to process unstructured data at scale. 3. **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 permissions across different departments. ### **Qualifications** **Minimum Qualifications:** - **Education:** Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience. - **Software Engineering:** 3+ years of experience in Python development, with strong proficiency in API development (FastAPI/Django) and building integrations between SaaS platforms. - **AI/ML Application:** Proven experience building applications using LLM APIs and orchestration frameworks, Experience with "Agent" concepts. - **Data Proficiency:** Strong SQL skills and familiarity with cloud data warehouses (Snowflake/BigQuery). **Preferred Qualifications:** - **Business Systems Knowledge:** Experience working with APIs for major B2B tools (Salesforce, HubSpot, Zendesk, Jira, Marketo). - **Workflow Automation:** Experience with tools like Airflow, Zapier/Make (advanced usage), or custom workflow engines. - **B2B Domain Expertise:** Understanding of the interplay between Sales, Product, and Customer Success. ### **Competencies** - **The "Builder" Mindset:** You are comfortable taking a high-level strategic requirement from leadership (e.g., "We need to automate lead qualification") and independently architecting and coding the solution. - **Systemic Thinking:** You understand how a change in product data schema impacts the downstream marketing automation flow. - **Adaptability:** You can switch contexts rapidly—from debugging a SQL query for Strategy to refining a prompt for Customer Support automation.