1118 | Senior AI-Native Engineer
RemoteArgentina or Colombia
Job Summary
Lead agentic development systems by defining executable SDD/BDD specs and driving adoption of AI-first practices using tools like Cursor and Copilot across the full SDLC. Apply critical judgment to AI-generated code and attestations before merge, assessing correctness, security, and edge cases while shaping team norms for spec-driven delivery. Own end-to-end execution of complex features in high-volume SaaS environments, designing reliable solutions and surfacing systemic risks early through rigorous unit and integration testing. Mentor junior engineers and collaborate with product, design, and QA to align global distributed teams on AI-native engineering standards.
Required Qualifications
- 5+ years of experience developing enterprise or SaaS software in production environments
- Personal fluency with agentic coding tools (Claude Code, Cursor, Copilot, or equivalents) — you use these in your daily work and real delivery scenarios, not as an experiment
- Proven experience creating and using agentic development systems with genuine rigor — spec-first workflows, attestation practices, BDD/SDD — this is how we work, not a practice you're learning on the job
- Demonstrated track record of shipping complex features independently, end-to-end
- Proficiency in modern backend/full-stack technologies, with strong experience in Golang, PHP, or Node.js/TypeScript ecosystem, along with solid unit/integration testing skills
- Experience with RESTful API design and consumption
- Working knowledge of SQL and NoSQL data stores
- Experience producing and maintaining automated test suites (unit, integration, or end-to-end)
- Experience working in Agile delivery environments with CI/CD pipelines
- Strong written communication skills; able to write clear specs, meaningful code review feedback, and async updates for a global team
Desired Qualifications
- Experience with cloud platforms (AWS or equivalent) and microservices architectures
- Familiarity with containerization, infrastructure-as-code, or observability tooling
- Experience developing or integrating with AI/ML features in a production SaaS product
- Background in B2B SaaS, CRM, or revenue technology
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