AI Product Engineer

Mindtickle; Bengaluru; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Mindtickle in Bengaluru, India seeks an AI Product Engineer to architect and build the company’s central intelligence engine. You will drive automated intelligence systems that retrieve insights, trigger workflows, and synthesize strategic intel across the business landscape.

The role blends software engineering, AI engineering and data analytics to deliver scalable middleware, agentic workflows with LLMs, and governance-driven data pipelines, partnering with analytics and security teams.

Qualifications

• Bachelor’s degree in CS/Engineering or equivalent practical experience.
• 3+ years in Python with API development and SaaS integrations.
• Experience with LLM APIs and agent-like workflow concepts.
• Strong SQL skills and familiarity with Snowflake/BigQuery.

Responsibilities

• Architect and build the Enterprise Brain and integrations.
• Implement Agentic workflows with LLMs and guardrails.
• Ensure real-time data pipelines and governance across teams.

Skills

Python development

API development

LLM APIs

Agent concepts

SQL

System design

B2B domain knowledge

Education

Bachelor’s degree in Computer Science or Engineering

Tools

FastAPI

Django

Salesforce

HubSpot

Zendesk

Airflow

Zapier/Make

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.