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Open nowPosted 57 days ago

AI Product Engineer

Mindtickle19 open roles

Where
Pune, Maharashtra
Work mode
Hybrid
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Your applicationOpen nowAI Product EngineerMindtickle · Pune, Maharashtra

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on Mindtickle's own form.

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The clock on this job

Early applications get read.

13.4% of postings close within 7 days. Measured by our own scanner across the market. Mindtickle postings stay open a median of 62 days.

Share of postings closed within
  1. 2.2%1 day
  2. 4.6%3 days
  3. 13.4%7 days
  4. 21.6%14 days
  5. 36.2%30 days
  6. 59.0%60 days
This job: posted 57 days ago

Mindtickle median: 62 days open

The posting

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.
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