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
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 new models, frameworks, and patterns as they emerge and bring the useful ones in
How we will take care of you:
Great working environment
Rapid career development opportunities
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
How we will take care of you:
Great working environment
Rapid career development opportunities
