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 perks.

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

-
Own AI-powered automations across the SDLC: spec-to-code workflows, automated PR review, QA generation, release-time checks

- 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

-
Set patterns and guardrails: prompt management, tool security, rate limits, dry-run/safety modes for destructive operations

- Stay close to the frontier - evaluate recent models, frameworks, and patterns as they emerge and bring the useful ones in

How we will take care of you:

-
Motivating compensation

-
Medical & Life insurance

-
Paid Holidays

-
Great working environment

-
Rapid career development opportunities

Requirements

Must Haves:

-
4+ years of backend or full-stack engineering experience, with at least 1 year focused on LLM applications, agents, or AI-powered automation in production

-
Strong proficiency with Python and/or TypeScript/Node.js

-




Hands-on experience with the LLM application stack: OpenAI/Anthropic APIs, function calling/tool use, structured outputs, streaming

-
Familiarity with MCP (Model Context Protocol) or comparable agent-tooling protocols and frameworks (LangGraph, LlamaIndex, custom orchestration)

- 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

Nice to haves:

-
Experience operating Claude Code, Cursor, or similar agentic coding tools at team or org scale

-
Background in QA automation, test generation, or developer productivity tooling

-
Familiarity with Apollo Federation, GraphQL subgraph architecture

-
Experience with workflow/rules engines, DSLs, or no-code platforms

-
Track record of shipping AI features that customers (not just internal users) actually adopt

-
Familiarity with Playwright, Bruno, mabl, k6, or similar testing tools

How you'll be measured:

- AI capabilities shipped to internal teams and customers - adoption, reliability, retention of use

-
Engineering velocity unlocked across the org through automation

-
Operational health of AI systems: cost per outcome, accuracy/eval scores, incident rate

-
Quality of the patterns and primitives you establish for others to build on

Benefits

How we will take care of you:

-
Motivating compensation

-
Medical & Life insurance

-
Paid Holidays

-
Great working environment

-
Rapid career development opportunities

📌 AI Automation Engineer (India)
🏢 HRhelpdesk
📍 India