As technical practitioners transition to senior, staff, or principal roles, they often encounter a hidden career bottleneck: the diminishing number of colleagues capable of meaningfully challenging their reasoning. This potential isolation can hinder professional growth and decision-making in critical project phases.
InfoQ’s online certification cohorts are designed specifically to bridge this gap by bringing together small groups of senior engineers from diverse companies and industries to collaboratively navigate real-world decisions in a confidential environment. This unique format fosters an enriching learning experience where participants can openly discuss their challenges and insights.
One such initiative is the launch of the InfoQ Certified AI Engineering Program. This specialized online cohort features live sessions, held 4 hours a week over 5 weeks, tailored for senior engineers, software architects, AI/ML platform engineers, technical leads, and engineering managers involved in production AI systems. The inaugural cohort kicks off on July 25, 2026, with weekly live sessions each Saturday at 9:00 AM PDT.
The program is facilitated by Hien Luu, a Sr. Engineering Manager at Zoox and author of acclaimed books like MLOps with Ray and Beginning Apache Spark 3. As the leader of the Machine Learning Platform team at Zoox, Luu brings extensive experience in AI/ML infrastructure and production engineering, having presented at prominent QCon conferences.
“Most teams are making infrastructure, platform, and reliability decisions for production AI systems before they have strong internal benchmarks for what good looks like. The cohort gives senior engineers a way to apply proven frameworks to their own work, with experienced peers from other organizations challenging their assumptions and trade-offs.”
— Hien Luu, InfoQ Certified AI Engineering Cohort facilitator
From AI Prototypes to Production Systems
Transitioning AI features from prototype stages to production entails a critical shift in engineering focus. The essential questions evolve from whether a system can function in isolation to whether it can operate reliably at scale under production constraints. This transition can be daunting, especially when decisions about retrieval architecture, context pipelines, agent orchestration, and evaluation methods are made with limited external feedback.
Within each cohort, participants, who are seasoned engineers from different organizations, leverage proven frameworks to tackle the AI engineering decisions they face. This collaborative environment allows them to share both successful strategies and lessons from past mistakes, ultimately gaining newfound insights or reassurance about their current pathways.
With small group sizes and confidentiality at the forefront, participants can explore difficult architectural decisions, technical debts, and organizational limitations without fear of judgment. This intimate setting fosters a rich dialogue that sparks creativity and innovation.
The Curriculum
The curriculum is structured around engaging four-hour live online sessions, allowing participants to delve into real problems from their work rather than confining discussions to generic case studies. Here’s a breakdown of the weekly focus:
- Week 1: Becoming an AI-Native Engineering Team – Participants analyze how AI transforms engineering habits, informs product thinking, and influences architectural trade-offs, all while considering existing resilience practices.
- Week 2: Designing and Building RAG and Context Pipelines – This week dives into retrieval architectures, knowledge graphs, and memory pipelines, ensuring adaptability as data evolves and query complexities rise.
- Week 3: Designing and Building AI Agents – Focusing on tools varying from single-purpose to multi-agent systems, this session explores the balance between autonomy and control in production settings.
- Week 4: AI Platforms and Infrastructure – Participants discuss platform design layers for AI systems, including centralization versus federation, and efficient routing of batch and real-time workloads while managing costs.
- Week 5: AI Operational Excellence: Evals, Trust, and Reliability – This final week emphasizes evaluation frameworks and operational strategies that ensure AI systems remain dependable post-deployment.
Throughout the program, participants will create a technical capstone article, with drafts presented in Week 5. The standout articles have the opportunity for publication on InfoQ, providing additional recognition for successful contributors. Graduates of the program will also earn the coveted InfoQ Certified AI Engineering Program certification, which can enhance their professional profiles on LinkedIn.
Upcoming Online Cohorts and Certifications
Investing in professional development is crucial, and most companies offer reimbursement for such initiatives. To assist in securing support from your manager, InfoQ provides a handy “Convince Your Boss” template that outlines the business case for enrolling in this valuable program.
For more information on enrollment and details about the various certification programs, visit the InfoQ Online Certification Program page.
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