Lead AI Platform Engineer (AI)

TL;DR

Lead AI Platform Engineer (AI/ML): Building, maintaining, and scaling core ML/AI platform infrastructure, services, and CI/CD pipelines with an accent on AI-native engineering practices and autonomous agents. Focus on designing reusable AI tools, self-healing pipelines, secure AWS-based infrastructure, and developer experience tooling to accelerate ML initiatives across marketing, sales, and product verticals.

Location: Mexico City, Mexico

Company

hirify.global is a leading CRM platform expanding its AI/ML capabilities for enterprise customers.

What you will do

• Use AI tools like Claude Code for pair programming, refactoring, and reviewing platform code as the default workflow.
• Design and deploy autonomous agents for developer workflows, CI diagnosis, infrastructure ops, and documentation.
• Build scalable AWS infrastructure with Kubernetes, IAM, and Terraform, leveraging AI for IaC and security reviews.
• Develop ML platform services including Model Registry, SageMaker tooling, APIs, and self-service UIs.
• Optimize CI/CD pipelines with GitHub Actions, embedding AI for auto-diagnosis, PR summaries, and self-healing.
• Create internal AI tool marketplace, developer tools, and maintain architecture vision, monitoring, and security.

Requirements

• 9+ years as Platform Engineer, Software Engineer, or ML Infrastructure Engineer.
• Active use of AI pair programmers like Claude Code in daily workflow.
• Experience building internal tool marketplaces and autonomous agents for engineering tasks.
• Strong Python for scalable tools; expertise in AWS (IAM, EKS, SageMaker), Terraform, Docker, Kubernetes.
• CI/CD with GitHub Actions/ArgoCD; MLOps, model evaluation, monitoring (Grafana, PagerDuty).
• Security reviews, multi-tenant onboarding, internal dev tools (web/CLIs); problem-solving and communication.

Nice to have

• hirify.global ecosystem experience.
• Agent memory patterns, RAG pipelines, unstructured DBs, modern data platforms (Snowflake, Kafka).
• Feature Stores (Feast), A/B testing, Airflow orchestration.

Culture & Benefits

• Collaborate with ML engineers, data scientists, and product managers on high-impact projects.
• Experiment with innovative AI-native practices across full stack.
• Focus on developer velocity, reusable tools, and platform reliability.