InfoQ Launches Online AI Engineering Cohort and Certification for Senior Software Practitioners

2026-05-22 · Source: InfoQ · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Advanced, short

Summary

InfoQ has launched the "InfoQ Certified AI Engineering Program," an online certification cohort designed for senior software practitioners, including senior engineers, software architects, AI/ML platform engineers, technical leads, and engineering managers working on production AI systems. This 5-week program, starting its first cohort on July 25, 2026, with weekly 4-hour live sessions on Saturdays at 9:00 AM PDT, aims to bridge the gap in challenging senior-level reasoning for AI system development. Facilitated by Hien Luu, Sr. Engineering Manager at Zoox and author, the curriculum covers critical areas like becoming an AI-Native Engineering Team, designing RAG and Context Pipelines, building AI Agents, AI Platforms and Infrastructure, and ensuring AI Operational Excellence. Participants apply proven frameworks to their own work, develop a technical capstone article, and receive certification.

Key takeaway

For AI Architects or MLOps Engineers scaling production AI systems, consider InfoQ's Certified AI Engineering Program to validate your architectural decisions and operational strategies. This 5-week cohort offers a confidential peer environment to apply proven frameworks to your real-world challenges, ensuring your systems are predictable and cost-effective. You will gain new approaches or confirm existing ones, reducing risks in critical areas like RAG, agent orchestration, and platform design.

Key insights

Senior practitioners can validate and refine production AI system decisions through structured peer-group application of proven frameworks.

Principles

• Senior roles benefit from external peer challenge.
• Production AI demands predictable, scalable system design.
• Confidential peer groups improve architectural decision-making.

Method

A 5-week online cohort with 4-hour weekly live sessions, applying frameworks to participants' real-world AI engineering problems, culminating in a technical capstone article.

In practice

• Apply frameworks to RAG and context pipeline design.
• Evaluate tradeoffs for AI agent orchestration.
• Design AI platforms for cost-effective inference.

Topics

• AI Engineering
• Production AI Systems
• MLOps
• RAG Architectures
• AI Agents
• AI Platforms

Best for: AI Engineer, MLOps Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by InfoQ.