Senior Rails Engineer, AI Enablement

This is a fully remote position, open to applicants in Latin America.

📋 Description

• Develop and deploy features utilizing a robust Ruby on Rails backend hosted on Heroku alongside two React applications hosted on Vercel.

• Collaborate within a structured monolithic architecture that encompasses state machines, warehouse workflows, device records, and third-party integrations.

• Maintain a balance between AI initiatives and the platform's reliability, maintainability, performance, and architectural standards.

• Create shared AI infrastructure that spans across products and internal developer tools.

• Establish benchmarking frameworks and evaluation harnesses using historical company data prior to deployment.

• Implement confidence thresholds, input/output constraints, usage guidelines for tools, audit logs, and mechanisms for safe failure or human escalation.

• Develop AI-assisted workflows for device data normalization, serial number resolution, and hardware classification.

• Create automated customer-support triage, semantic deduplication, and Linear ticket generation for product defects.

• Execute retrieval-augmented generation and search functionalities over data related to physical assets, resale, and processing.

• Design permissioned agents for operational tasks such as record updates and shipment verification.

• Integrate AI tools into engineering, QA, and DevOps processes.

• Advise product leaders and engineers on practical AI applications, limitations, and trade-offs.

⛳️ Requirements

• Over 5 years of professional production experience in Ruby on Rails.

• Extensive knowledge of large monolithic architectures.

• Demonstrated success in deploying LLM/ML features to production using retrieval-augmented generation, structured outputs, tool-calling agents, and classification techniques.

• Essential experience in developing AI evaluation and benchmarking frameworks.

• Proven history of designing confidence thresholds, workflows for human-in-the-loop reviews, and strategies for error mitigation.

• Practical React experience for testing, debugging, and frontend integration.

• Familiarity with handling messy or incomplete datasets, including entity resolution and data normalization.

• Availability to cover at least 75% of EST business hours.

• Experience in fine-tuning or adapting models using proprietary datasets.

• Background in serial numbers, hardware asset tracking, product catalogs, or physical logistics systems.

• Experience in implementing AI adoption strategies within established engineering teams.

• A pragmatic, measurement-driven approach to engineering.

• Capability to determine when deterministic or traditional software is safer and more effective than AI.

🏝️ Benefits

• A competitive salary and performance-based incentives.

• Comprehensive health and wellness benefits.

• Opportunities for professional development and career advancement.

• Flexible work hours and remote work options.

• A dynamic and inclusive work environment.