### **Job Information**
Job Opening ID
ZR\_782\_JOB
Date Opened
05/29/2026
Industry
IT Services
Work Experience
5\-10 years
Job Type
Full time
Salary
Confidential
City
Indore
State/Province
Madhya Pradesh
Country
India
Zip/Postal Code
452001
### **Job Description**
The Company, a USA Subsidiary is a rapidly growing, private equity\-backed SaaS company founded by engineers, focused on building scalable, high\-quality products. Our solutions support over 3000 organizations, enabling them to manage grants, scholarships and philanthropic initiatives effectively. We offer cloud\-based platforms that power the end\-to\-end lifecycle of grants, scholarships, fellowships, employee giving, and volunteer programs. In our organization, we foster a collaborative, innovation\-driven culture with a flexible work environment and competitive benefits. **About the role**
We're building the AI layer that makes our grant management platform \- and the people who build it \-more leveraged. We've already shipped the foundations: an internal MCP server with a growing tool catalog, a shared context layer that grounds agents in real product data, and a QA AIification initiative that's moving testing from local dev into staged release. We need an engineer to push this work forward
end\-to\-end.
You'll design, build, and operate agentic systems and AI\-powered automations that touch real production workflows \- both internal (engineering, QA, support tooling) and customer\-facing (workflow rules, document processing, intelligent assistance inside the product). This is a hands\-on builder role for someone who's excited about LLMs as a serious engineering substrate, not a demo. **Responsibilities:*** Design, build, and maintain MCP servers and tools that expose our internal systems (MongoDB, GraphQL APIs, internal services) to LLM agents safely and usefully
\- Build customer\-facing AI features \- for example, replacing complex DSL\-based rule engines with plain\-English LLM\-driven workflow creation* Develop the shared context layer: retrieval, grounding, prompt assembly, and the evaluation harness that keeps it honest
\- Implement evals, regression tests, and observability for LLM systems \- latency, cost, accuracy, hallucination rate, drift* Partner with product engineering teams to integrate AI capabilities into the Node.js / React/GraphQL / MongoDB / Apollo Federation stack
\- Stay close to the frontier \- evaluate new models, frameworks, and patterns as they emerge and bring the useful ones in **How we will take care of you:*** Motivating compensation
### **Requirements**
\- Practical experience with retrieval (vector or hybrid), prompt engineering, and LLM evaluation \-you know how to make these systems reliable, not just functional\- Solid software engineering fundamentals: testing, observability, code review, incident response* Comfort with MongoDB or similar NoSQL databases, and with REST/GraphQL APIs
\- AI capabilities shipped to internal teams and customers \- adoption, reliability, retention of use* Engineering velocity unlocked across the org through automation
### **Benefits**
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