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Mindtickle is transitioning to an AI-first operational model and seeking an AI Product Engineer to architect and build the company's central intelligence engine. This is a high-impact, hands-on role requiring convergence of three skill sets: software engineering (50%), AI engineering (30%), and data analytics (20%).
Key Responsibilities:
Building the "Enterprise Brain" — 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 and 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 (retrieval-augmented generation) 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 underlying data pipelines support real-time or near-real-time AI applications. Maintain security and privacy standards, ensuring AI agents respect data access permissions across departments.
You will be the primary builder of automated intelligence systems—tools that not only retrieve insights but actively trigger workflows and synthesize strategic intelligence across the entire business landscape. This role demands a builder's mindset: comfort taking high-level strategic requirements from leadership and independently architecting and coding solutions. You must think systemically (understanding how product data schema changes impact downstream marketing automation) and adapt rapidly across contexts (debugging SQL queries for strategy, refining prompts for customer support automation).
Requirements:
Minimum Qualifications:
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
- 3+ years of Python development experience with strong proficiency in API development (FastAPI/Django) and building integrations between SaaS platforms
- Proven experience building applications using LLM APIs and orchestration frameworks; experience with agent concepts
- Strong SQL skills and familiarity with cloud data warehouses (Snowflake/BigQuery)
Preferred Qualifications:
- Experience working with APIs for major B2B tools (Salesforce, HubSpot, Zendesk, Jira, Marketo)
- Experience with workflow automation tools like Airflow, Zapier/Make (advanced usage), or custom workflow engines
- B2B domain expertise and understanding of interplay between Sales, Product, and Customer Success
