Senior Ruby On Rails Engineer

Job Summary

The Senior Ruby on Rails Engineer designs, scales, and maintains high-traffic backend systems while intentionally integrating AI/LLM workflows. Core responsibilities include authoring high-performance Rails architectures, designing clean APIs, managing background job worker patterns, constructing retrieval and prompt context strategies, writing comprehensive automated test suites, and resolving complex, live production incidents.

Technical Stack Environment

While centered on your core domain, your everyday environment will interact across these systems:

• Backend Core: Ruby on Rails (Latest stable enterprise versions).
• Data Layer: Relational Databases (PostgreSQL/MySQL), Redis for caching, and advanced Key-Value stores.
• Asynchronous Pipeline: Sidekiq / Resque / ActiveJob for high-volume background queue processing.
• AI & Orchestration: OpenAI API, Anthropic Claude, LangChain/Ruby-equivalent orchestration layers, vector embeddings, and prompt construction models.
• Testing Suite: RSpec, Capybara, FactoryBot, CI/CD automated pipeline runners.

Experience & Competencies

Candidates must demonstrate a senior-level track record of building production systems and a pragmatic understanding of AI application architecture:

Core Rails & Backend Mastery

• Professional Tenure: Solid, provable years of professional software engineering experience centered on Ruby on Rails, operating at a Senior or Lead level.
• Production Scale: Documented history of building, scaling, and running production web applications used by active, real-world customer bases.
• Database & API Design: Deep backend expertise, including advanced relational database design, query optimization, secure RESTful/GraphQL API construction, and database connection pooling.
• Asynchronous Architecture: Advanced knowledge of background processing jobs, race conditions, event-driven architecture, and state-machine transitions.

Engineering Pragmatism & Testing Habits

• Problem Deconstruction: Proven capability to take ambiguous product requests, break them down into concrete requirements, and propose practical technical solutions.
• Defensive Testing Habits: Exceptional automated testing habits with a deep care for long-term code maintainability, demonstrating skill in authoring rigorous Unit, Integration, and End-to-End (E2E) test frameworks.
• Production Debugging: A disciplined, systematic mindset when tracking down live production anomalies, managing memory leaks, and addressing system bottlenecks.

AI/LLM Literacy & Judgment

• GenAI Operational Familiarity: Direct hands-on interest or professional experience working with LLM APIs, prompt engineering, and context strategies (e.g., retrieval-augmented patterns, token limitation handling, embedding search).
• Architectural Trade-Off Awareness: Clear judgment regarding the balance of deterministic software design versus probabilistic AI models, ensuring AI features are applied only when they directly improve product quality or user workflows.
• Cost & Latency Management: Understanding the operational trade-offs unique to AI workloads, specifically managing API costs, token volume, model response latencies, and fallback execution logic.

Key Responsibilities

1. End-to-End Technical Ownership & System Architecture (35%)

• Lead Feature Lifecycle: Own the entire lifecycle of complex backend features, starting from initial technical design documents through code implementation, testing, and production monitoring.
• Scale Rails Systems: Build and maintain highly reliable, scalable Ruby on Rails backend infrastructures capable of handling high transaction volumes.
• Optimize Performance: Audit system performance constantly, optimizing slow database queries, fine-tuning Redis caches, and structuring background jobs to minimize main-thread latency.

2. Pragmatic AI/LLM Integration & Workflow Engineering (30%)

• Design AI Workflows: Architect and deploy smart AI/LLM features that automate user workflows and improve product features without sacrificing application stability.
• Manage Prompt Contexts: Build context strategies, prompt layouts, and retrieval layers to ensure LLM integrations return accurate, secure, and contextually rich data.
• Assess Engineering Value: Evaluate incoming AI requests with a critical eye, steering the product toward simple, deterministic programming when an LLM model introduces unnecessary cost or complexity.

3. Code Quality, System Security & Automated Testing (20%)

• Enforce Test Discipline: Write and maintain comprehensive test coverage across the application using RSpec, including isolated unit tests, multi-component integration tests, and full E2E user flows.
• Uphold Best Practices: Participate in rigorous code reviews, acting as a technical mentor to elevate team patterns around long-term code quality, security standards, and Rails conventions.
• Mitigate Vulnerabilities: Design secure endpoints and data pipelines, proactively neutralizing common vulnerabilities such as SQL injections, prompt injections, and data access cross-overs.

4. Production Reliability & Operational Support (15%)

• Debug Live Issues: Jump directly into active production clusters to isolate, debug, and resolve complex application crashes or network bottlenecks.
• Monitor Infrastructure Health: Use monitoring and logging infrastructure to analyze API latency metrics, error frequencies, background queue backlogs, and external vendor dependencies.
• Foster a Growth Mindset: Maintain a responsible, curious, and highly flexible approach—stepping up willingly to deploy updates, investigate system anomalies, and own final system outcomes.

Expected Outputs & Deliverables

• Scalable, production-ready Ruby on Rails code covered by modular automated test blocks.
• Clean technical architecture blueprints detailing data schemas, background worker pipelines, and API pathways.
• Operational, cost-effective LLM feature wrappers built with robust fallback logic and prompt tracking frameworks.
• Clear root-cause analyses (RCAs) for any production incidents resolved, along with corresponding monitoring alerts.

Skills Required:

• Computer / Software / It / Data