Principal AI/ML Architect (AWS)

TL;DR

Principal AI/ML Architect (AWS): Leading end-to-end ML assessments and designing production-scale AI systems for diverse clients with an accent on the AWS ecosystem, GenAI, and MLOps. Focus on architecting foundation model adaptations, RAG patterns, and translating complex ML tradeoffs into business value.

Location: Remote (Mexico). Note: Visa sponsorship is not provided.

Company

A cloud native services company that helps organizations optimize their people and technology using Amazon Web Services (AWS).

What you will do

• Lead end-to-end ML assessments across infrastructure, data pipelines, and model lifecycles to drive executive decision-making.
• Serve as the senior technical authority on client engagements, providing architectural guidance and ensuring technical quality.
• Design and orchestrate high-quality POCs to validate ML initiatives for customers.
• Advise customers on MLOps and LLMOps standards, including pipeline design and production monitoring frameworks.
• Collaborate with sales and solutions teams to shape technical depth in proposals and statements of work.
• Strengthen internal ML practice through peer guidance, technical interviews, and development of reference architectures.

Requirements

• 10+ years of experience in AI/ML with a proven track record in client-facing consulting or advisory roles.
• Deep expertise in the AWS ML and GenAI ecosystem, specifically SageMaker and Bedrock.
• Strong proficiency in at least two ML domains (e.g., NLP, Computer Vision, Time Series).
• Proven ability to architect production ML systems end-to-end, including MLOps and LLMOps.
• Expertise in foundation model adaptation (fine-tuning, RLHF), RAG, and agentic system design.
• Must be based in Mexico; no visa sponsorship available.

Nice to have

• AWS Certified Machine Learning Specialty or AWS Certified Solutions Architect Professional.
• Experience developing practice-level standards and reusable ML accelerators across multiple engagements.
• Fluency in responsible AI practices, including model evaluation, bias detection, and AI governance.
• Hands-on experience designing and deploying SRE agents and AI-driven operations workflows in production.

Culture & Benefits

• 100% remote work environment.
• Private health insurance and flexible time off.
• Competitive phantom equity.
• Stipends for equipment, office setup, and professional development.
• Paid certifications and exams.