Senior Backend Software Engineer @ Shelf
(práce v Polsku)

Senior Backend Software Engineer @ Shelf

Polsko

• 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 - youve 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 dont wait for instructions but proactively identify problems and propose solutions.
• Youre 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

Job Description:

As a Backend Engineer at Shelf, youll architect and build the distributed systems that make enterprise AI actually work. While others are chasing AI hype, were solving the unglamorous but critical problem that determines whether AI succeeds or fails: data quality at scale.

You wont just write code—youll 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. Youll 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 were building the quality layer for AI itself. When your code elegantly handles edge cases, gracefully degrades under load, and self-monitors for anomalies, youre not just meeting engineering standards - youre directly preventing AI hallucinations and ensuring accurate answers for millions of end users.

Were a product company that ships fast without compromising on lasting quality. Youll work alongside proactive, ever-learning engineers in an environment where AI isnt just what we build—its how we build.

The best engineers we know are drawn to problems that matter. If youre 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 hasnt figured out yet.

What Shelf Offers:

• B2B contract.
• Company Stock Options.
• Hardware: MacBook Pro.
• Modern technical stack. Develop open-source software.
• GitHub Copilot subscription.
• Access to Claude Code, OpenAI Codex, TypingMind, and MCP Servers.

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 havent deployed. Why? Poor data quality - it is the #1 obstacle companies have in getting GenAI projects into production.

Weve 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:

• 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
• Weve 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

,[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: Python, Storage, SQL, NoSQL, Performance tuning, AI, Cloud, AWS, Azure Cloud, NLP, GitHub Copilot, Node.js, TypeScript, CQRS, Claude, OpsLevel, RAG, ML Additionally: Stock options, GitHub Copilot subscription, LLM credits.

Kategorie

backend

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