# AI Product Engineer at Mindtickle

AI Product Engineer at Mindtickle in Pune, Maharashtra and 1 more. Hybrid, full-time. Posted September 9, 2026.

- Company: Mindtickle
- Location: Pune, Maharashtra and 1 more
- Workplace: Hybrid
- Job type: Full-time
- Experience: 3+ years
- Posted: 2026-09-09
- Apply: https://jobs.lever.co/mindtickle/864ac315-d7c5-4cd5-ab44-0e33d08f48f1/apply
- Source: https://jobmagic.ai/jobs/ai-product-engineer-mindtickle-10ezwe

## Requirements

- 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).

## 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:

- **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 Responsibilities1. 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.

### **QualificationsMinimum 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.