AI Product Engineer

About the Role

We are transitioning our organization to an AI-first operational model and are seeking an AI Product Engineer to architect and build the company’s central intelligence engine. This high-impact, hands-on engineering role focuses on building automated intelligence systems that retrieve insights, trigger workflows, and synthesize strategic intelligence across the business.

This role requires 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

• 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).
• 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.
• 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: Comfortable taking high-level strategic requirements from leadership and independently architecting and coding solutions.
• Systemic Thinking: Understand how a change in product data schema impacts downstream marketing automation flows.
• Adaptability: Ability to switch contexts rapidly—from debugging a SQL query for Strategy to refining a prompt for Customer Support automation.