Senior Go Engineer (AI)

TL;DR

Senior Go Engineer (AI): Designing and building backend systems that power AI-native platform workflows at enterprise scale with an accent on integrating LLMs, automating quality pipelines, and enabling event-driven architectures. Focus on owning hard problems end-to-end and building at the intersection of Go backend engineering and production AI.

Location: Remote (Latin America)

Company

hirify.global helps U.S. companies build and scale world-class AI, ML, and Data teams, powered by the top 1% of LATAM talent.

What you will do

• Architect, design, and implement production-grade backend services in Go that integrate with LLMs.
• Build AI-augmented CI/CD pipelines and automated quality gates.
• Implement agentic architectures for complex multi-step workflows and AI-assisted automation pipelines.
• Design and build event-driven services using Kafka for cross-service data propagation and real-time workflow orchestration.
• Define and enforce quality standards for AI-generated artifacts, ensuring outputs verify real behavior and meet production reliability requirements.
• Partner with platform and product teams in a collaborative environment, driving architecture decisions and implementations end-to-end.

Requirements

• 5–7 years of professional experience as a Software Engineer, with strong Go (Golang) proficiency as the primary language.
• Hands-on production experience with AI/LLM integration.
• Experience building or working with contract-first development workflows (OpenAPI, Avro, or schema-driven).
• Experience with event-driven architectures and Kafka for cross-service data propagation.
• Practical experience with AWS, including EKS and container-based deployments.
• Experience designing systems for multi-tenant or hierarchically structured data models, and working with PostgreSQL in production environments.
• Excellent English communication skills, both written and spoken, with the ability to collaborate across distributed teams.

Nice to have

• Experience with AI-augmented CI/CD pipelines, automated quality gates.
• Experience with structured data extraction from unstructured inputs using LLMs, or knowledge engineering for AI tools.
• Familiarity with workflow orchestration tools such as Temporal or Airflow; experience with Kubernetes (EKS) deployment and operations.
• Exposure to knowledge graphs, ontologies, or graph-based data modeling; experience with SCIM, SAML, or enterprise identity provisioning patterns.

Culture & Benefits

• Ownership through equity participation.
• Annual company retreat.
• Education bonus for continuous learning.
• Company-wide winter break.
• Paid time off.
• Optional in-person events and meetups.