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We are hiring two Innovation Engineers to form the founding team of Kriyadocs R&D. These are hands-on senior full-stack roles for engineers who are excited by the frontier - building POCs, validating new technologies, and graduating high-quality solutions into the core platform. You will work in a small, high-autonomy environment with a clear mandate: explore fast, validate rigorously, and ship things that matter. Your output will directly shape the future of the product.
Job Responsibility:
Build and validate POCs - design and develop proof-of-concept solutions for AI-native features, automation workflows, and platform innovations relevant to scholarly publishing
Own the full stack - from data pipelines and backend APIs to frontend interfaces and integration layers
Build with AI tooling daily - use Claude Code, MCP servers, and agentic workflows as primary development tools
Graduate experiments to Engineering - produce clean, well-documented, production-worthy handoffs that Engineering squads can confidently adopt and extend
Evaluate emerging technologies - stay ahead of the curve on AI/ML tooling, document processing, and SaaS platform patterns relevant to the scholarly publishing domain
Collaborate with Staff Engineer and VP of Engineering - ensure R&D output aligns to platform architecture and strategic priorities
Requirements:
4 - 6 years of full-stack engineering experience with strong proficiency in Node.js / TypeScript and a modern frontend framework (React preferred)
Hands-on experience building with AI tools - Claude Code, OpenAI / Anthropic APIs, LangChain, MCP, or similar
Comfort with ambiguity - you can take a loosely defined problem, structure it into a testable experiment, and deliver a validated outcome without hand-holding
Strong engineering fundamentals - clean code, thoughtful API design, testing discipline, and documentation habits that make your work easy to hand off
Experience with cloud infrastructure (AWS preferred) and modern deployment patterns (Docker, ECS, or equivalent)
A bias for shipping - you move fast, learn from feedback, and iterate without losing sight of quality or production-readiness
Nice to have:
Experience with document processing pipelines - DOCX, XML, PDF transformation, MathML, or LaTeX
Familiarity with scholarly publishing workflows or academic content management systems
Prior experience in an R&D, innovation lab, or early-stage product environment
Interest in or prior contributions to AI-assisted development tooling, agentic systems, or LLM application development