Application Engineer (AI Automation)
Pure Storage · Bengaluru · 5+ yrs experience · Posted 2026-07-08
Tech stack: Java, JavaScript, Python, TypeScript
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About the role
We’re in an unbelievably exciting area of tech and are fundamentally reshaping the data storage industry. Here, you lead with innovative thinking, grow along with us, and join the smartest team in the industry.
This type of work—work that changes the world—is what the tech industry was founded on. So, if you're ready to seize the endless opportunities and leave your mark, come join us.
As an Application Engineer for AI Automation, you will be at the clinical edge of our intelligence layer. This isn't just about writing code; it's about architecting the nervous system of our automated enterprise. You will bridge the gap between heavy-duty enterprise platforms (Salesforce, Zuora, ServiceNow) and the next generation of Agentic AI.
Responsibilities:
- Architect MCP Servers: Design and implement robust MCP servers to expose Salesforce services (Quote, Opportunity, Renewal) and other core platforms to AI agents.
- Data Model Synthesis: Rapidly decompose the data models of enterprise applications like Zuora, Salesforce, and ServiceNow to build effective schemas for AI interaction.
- Build AI Pipelines: Develop and optimize Retrieval-Augmented Generation (RAG) workflows and AI agents that can execute complex tasks with minimal human intervention.
- Agentic Orchestration: Lead the transition from simple chatbots to Agentic AI, focusing on multi-agent collaboration and autonomous "Agent-to-Agent" communication.
- Integration Engineering: Build and maintain secure, scalable integrations using REST APIs across disparate cloud environments.
- Technical Leadership: Act as a subject matter expert for AI automation, guiding the evolution of our automation strategy and mentoring junior engineers.
Qualifications:
- 5+ years in professional software engineering.
- Expert-level proficiency in Python (for AI/ML orchestration) and JavaScript/TypeScript or Java (for enterprise services).
- Knowledge of Salesforce Sales Cloud (Apex knowledge is a plus, but understanding the API/Object model is mandatory).
- AI Specialization: Proven experience building AI agents using MCP (Model Context Protocol) and implementing RAG architectures.
- API Mastery: Deep expertise in designing and consuming RESTful APIs and handling complex authentication/OAuth flows.
- Autonomy: A "hunter" mindset for information—you can pick up a new tool like Zuora, read the docs, and understand the underlying data relationships without needing a guided tour.
- Familiarity with vector databases (Pinecone, Weaviate, or Milvus).
- Background in SalesOps automation (specifically Quote-to-Cash processes).
- Contributions to open-source MCP servers or AI frameworks.
Qualifications
- 5+ years in professional software engineering.
- Expert-level proficiency in Python (for AI/ML orchestration) and JavaScript/TypeScript or Java (for enterprise services).
- Knowledge of Salesforce Sales Cloud (Apex knowledge is a plus, but understanding the API/Object model is mandatory).
- AI Specialization: Proven experience building AI agents using MCP (Model Context Protocol) and implementing RAG architectures.
- API Mastery: Deep expertise in designing and consuming RESTful APIs and handling complex authentication/OAuth flows.
- Autonomy: A "hunter" mindset for information—you can pick up a new tool like Zuora, read the docs, and understand the underlying data relationships without needing a guided tour.
- Familiarity with vector databases (Pinecone, Weaviate, or Milvus).
- Background in SalesOps automation (specifically Quote-to-Cash processes).
- Contributions to open-source MCP servers or AI frameworks.
Responsibilities
- Architect MCP Servers:
- Design and implement robust MCP servers to expose Salesforce services (Quote, Opportunity, Renewal) and other core platforms to AI agents.
- Data Model Synthesis: Rapidly decompose the data models of enterprise applications like Zuora, Salesforce, and ServiceNow to build effective schemas for AI interaction.
- Build AI Pipelines:
- Develop and optimize Retrieval-Augmented Generation (RAG) workflows and AI agents that can execute complex tasks with minimal human intervention.
- Agentic Orchestration: Lead the transition from simple chatbots to Agentic AI, focusing on multi-agent collaboration and autonomous "Agent-to-Agent" communication.
- Integration Engineering: Build and maintain secure, scalable integrations using REST APIs across disparate cloud environments.
- Technical Leadership: Act as a subject matter expert for AI automation, guiding the evolution of our automation strategy and mentoring junior engineers.