Senior, staff, and principal technical roles often encounter a bottleneck: fewer people inside the organization can meaningfully challenge reasoning. InfoQ Certified AI Engineering Program is an online cohort designed to address that gap through small, confidential groups of senior engineers from different companies and industries working through real decisions together. The program targets senior engineers, software architects, AI/ML platform engineers, technical leads, and engineering managers building production AI systems. It runs for five weeks with live sessions four hours per week, starting July 25, 2026, with weekly Saturday sessions at 9:00 AM PDT. The cohort uses proven frameworks to help teams move from AI prototypes to predictable, scalable production systems, covering architecture, evaluation, platform boundaries, inference cost, observability, and operational ownership.
"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."
"As technical practitioners move into senior, staff, or principal roles, they frequently face a hidden career bottleneck: the number of people inside their organization who can meaningfully challenge their reasoning gets smaller. InfoQ's online certification cohorts are built around that gap: small groups of senior engineers from different companies and industries, working through real decisions together, in confidence."
"InfoQ has launched the InfoQ Certified AI Engineering Program, an online cohort with live sessions, 4 hours a week, for 5 weeks, for senior engineers, software architects, AI/ML platform engineers, technical leads, and engineering managers working on production AI systems. The first cohort begins on July 25, 2026, with weekly live sessions on Saturdays at 9:00 AM PDT."
"As teams move AI features from prototype to production, the core engineering questions shift. The challenge is no longer just whether a system can work once, but whether it can keep working predictably under production constraints and at scale. For many teams, the calls around retrieval architecture, context pipelines, agent orchestration, evaluation, platform boundaries, inference cost, observability, and operational ownership ar"
#production-ai-engineering #mlops #ai-infrastructure #evaluation--observability #engineering-leadership
Read at InfoQ
Unable to calculate read time
Collection
[
|
...
]