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.

Responsibilities

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 Python, leveraging AI coding assistants to accelerate development while maintaining high code quality
Transform ML models and LLMs into production-ready, reusable services with proper versioning, monitoring, and deployment pipelines
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 data pipelines across diverse storage solutions including S3, RDS/PostgreSQL, Elasticsearch, DynamoDB, and modern data warehouses

Requirements

Over 4 years of professional software engineering experience, including more than 1 year specializing in Python
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
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
Upper-Intermediate or better English skills for technical communication and documentation
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

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

Why Shelf:

GenAI will be at least a $4 Trillion market by 2032 and Shelf is a core infrastructure that enables GenAI to be deployed at scale
Our Leadership Team has deep knowledge management and AI domain expertise and enterprise SaaS background to execute this plan
We've been helping our customers prevent knowledge mismanagement since our founding in 2017
We have raised over $60 million in funding and our investors include Tiger Global, Insight Partners, Connecticut Innovations, and others
We have high velocity growth powered by the most innovative product in our category, 3X growth for 3 years in a row
We now have over 100 employees in multiple U.S. states and European countries, and we have ambitious hiring goals over the next few months

Steps in the recruitment process

Intro Call with a recruiter;
Tech Screen with an Engineer
Tech Interview(s) (could be 2 calls)
Final Interview

Senior Backend Python Developer

Shelf