TL;DR

Lead Fullstack Engineer (AI): Leading architecture, design, and development of end-to-end systems, integrating backend services with AI/ML pipelines and ensuring data consistency. Focus on designing scalable systems, maintaining observability, and automating recovery mechanisms across cloud environments.

Location: Brazil / Colombia / Costa Rica / El Salvador

Company

%hirify_global% is a rapidly scaling technology company specializing in working with and investing in prominent start-ups and brands in building and scaling their digital products.

What you will do

Lead architecture, design, and development end to end.
Integrate backend services with AI / ML pipelines, ensuring data consistency and system performance.
Collaborate closely with ML and data engineers to deploy, iterate, and monitor AI-powered features.
Design and maintain robust, scalable, systems capable of handling high traffic.
Ensure observability, monitoring, performance tuning, and automated recovery mechanisms are in place.
Own CI/CD pipelines and IaC (Terraform, CloudFormation, etc.) across cloud environments.

Requirements

12+ years of experience building and scaling systems (SaaS).
3-5 years working on Fullstack capability.
Expert proficiency in Python and frameworks like Django and/or FastAPI.
Strong proficiency in Javascript / Typescript with modern frameworks like ReactJs
Exposure to AI/ML or data-driven products (e.g. recommendation systems, search, classification)
Deep knowledge of API design (REST / GraphQL) and databases (PostgreSQL, Redis, or similar).
Proficiency with cloud infrastructure (AWS preferred) and infrastructure-as-code tools (Terraform, etc.).
Strong foundation in scalability, fault tolerance, performance tuning, and observability.
Excellent communication and documentation skills, able to explain complex concepts clearly.
Comfort working autonomously and making high-impact technical decisions in dynamic environments.

Nice to have

Startup or small-team experience (versus only large organizations).
Experience integrating or serving AI/ML models in production environments.
Familiarity with AI/ML ecosystems (e.g. OpenAI, Hugging Face, LangChain).
Exposure to backend data and infrastructure scaling for AI inference.
Open-source contributions or personal technical projects.
Ability to work across multiple cloud environments (AWS preferred).

Culture & Benefits

Remote first work environment
Competitive compensation
Laptop subsidy
Fixed subsidies/reimbursed expenses for healthcare, connectivity, and wellness.