Senior Backend Node.js Developer @ Shelf

Over 5 years of professional software engineering experience, including more than 3 year specializing in Node.js Deep understanding of distributed systems, concurrency patterns, and event-driven architectures Hands-on experience with AWS or Azure cloud primitives - you've personally provisioned resources, configured services, and built scalable systems using the full stack (compute, storage, messaging, databases). Cloud certifications are a plus Comfortable working with diverse data stores (SQL and NoSQL), including schema design and performance tuning at scale Write well-structured, testable code with thoughtful abstractions and interfaces Strong problem-solving skills and genuine curiosity - you don't wait for instructions but proactively identify problems and propose solutions. You're never satisfied with "good enough" and constantly refine your tools and approaches Upper-Intermediate or better English skills for technical communication and documentation Nice to have: Experience with AI coding assistants (GitHub Copilot, Claude, etc.) and eagerness to push the boundaries of AI-assisted development. Part of this role involves creating AI agents to automate portions of the engineering workflow Familiarity with service cataloging and documentation tools like OpsLevel or similar platforms Present your work effectively both verbally and visually, creating clear architecture diagrams and well-structured documentation Bonus: hands-on experience with NLP, unstructured data processing, Node.js/TypeScript, or RAG pipelines Bonus: experience with CQRS, distributed processing techniques, or building ML infrastructure Senior Backend Node.js Developer: As a Backend Engineer at Shelf, you'll architect and build the distributed systems that make enterprise AI actually work. While others are chasing AI hype, we're solving the unglamorous but critical problem that determines whether AI succeeds or fails: data quality at scale. You won't just write code — you'll design architectures that handle petabytes of unstructured data, provision and optimize databases across multiple cloud regions, and build services that process millions of documents daily. Every architectural decision you make ripples through to the accuracy of AI systems at companies like Glovo, Lufthansa, and Nespresso. This is systems engineering at its most demanding. You'll tackle challenges like building multi-tenant distributed systems with strict data isolation, implementing real-time data quality scoring at scale, and creating self-healing pipelines that maintain 99.9% uptime. Your services will integrate with everything from enterprise systems to cutting-edge LLMs, requiring both deep technical knowledge and pragmatic engineering judgment. We obsess over backend quality because we're building the quality layer for AI itself. When your code elegantly handles edge cases, gracefully degrades under load, and self-monitors for anomalies, you're not just meeting engineering standards - you're directly preventing AI hallucinations and ensuring accurate answers for millions of end users. We're a product company that ships fast without compromising on lasting quality. You'll work alongside proactive, ever-learning engineers in an environment where AI isn't just what we build — it's how we build. The best engineers we know are drawn to problems that matter. If you're excited by the challenge of building the infrastructure that makes the AI revolution actually deliver on its promises, this role offers the rare combination of technical depth, meaningful impact, and the prestige of solving problems that the industry hasn't figured out yet. What Shelf Offers: B2B contract Company Stock Options Hardware: MacBook Pro Modern technical stack. Develop open-source software Premier AI development environment: GitHub Copilot, Claude Code, OpenAI Codex, TypingMind, v0, MCP Servers, plus credits for experimenting with emerging AI tools About Shelf There is no AI Strategy without a Data Strategy. Getting GenAI to work is mission-critical for most companies, but 90% of AI projects haven't deployed. Why? Poor data quality - it is the #1 obstacle companies have in getting GenAI projects into production. We've helped some of the best brands like Glovo, Lufthansa, Herbalife, and Nespresso solve their data issues and deploy their AI strategy with Day 1 ROI. Simply put, Shelf unlocks AI readiness. We provide the core infrastructure that enables GenAI to be deployed at scale. We help companies deliver more accurate GenAI answers by eliminating bad data in documents and files before they go into an LLM and create bad answers. Shelf is partnered with Microsoft, Salesforce, Snowflake, Databricks, OpenAI and other big tech players who are bringing GenAI to the enterprise.  Our mission is to empower humanity with better answers everywhere. Why Shelf According to MarketWatch in November 2022, the global knowledge management market size was valued at US $405 Billion in 2021 and is expected to expand at a CAGR of 18.12% during the forecast period, reaching US $1.1 Trillion by 2027 Our Leadership Team has deep knowledge management and AI domain expertise and enterprise SaaS background to execute this plan We have raised over $60 million in funding and our investors include Tiger Global, Insight Partners, Connecticut Innovations, and others Our platform has been consistently rated #1 for overall usability by Gartner Digital Markets, and has received Easiest to Use, Easiest to Admin, and Highest Adoption awards from G2., and product of the year and innovation awards from leading publications, such as CIO Review. ,[Design efficient database schemas and distributed architectures that elegantly handle multi-tenant data isolation, horizontal scaling, and fault tolerance, Create comprehensive technical specifications and architecture diagrams that clearly communicate system design decisions and trade-offs, Define & maintain SLOs for your services and architect solutions that consistently meet them, even under peak load conditions, Develop modular, composable components using Node.js, leveraging AI coding assistants to accelerate development while maintaining high code quality, Instrument services for performance metrics, track processing throughput, and build automated data quality and consistency checks, Take full ownership of the systems you build, monitoring their health, optimizing performance, and ensuring they scale gracefully as data volumes grow, Stay current with industry best practices in distributed systems, data engineering, and AI infrastructure—experiment with new approaches and share your findings with the team, Contribute to our engineering standards and practices, mentor colleagues, and actively participate in our culture of continuous learning and improvement, Build and maintain REST APIs across diverse storage solutions including S3, RDS/PostgreSQL, Elasticsearch, DynamoDB, and modern data warehouses.] Requirements: AWS, Node.js, Azure Cloud, Cloud, SQL, NoSQL, AI, OpsLevel, NLP, RAG, TypeScript, CQRS Additionally: Stock options, GitHub Copilot subscription, LLM credits.