Building AI systems that hold up in production.
A five-week live cohort with Hien Luu for senior engineers, architects, and technical leads responsible for AI systems that have to hold up in production, not only in a demo.
The hard AI engineering calls start after the prototype works.
Getting a model to answer well in a demo is rarely the hard part. The harder calls come next: what the retrieval layer indexes, how much autonomy an agent gets, what the platform centralizes, which inference costs are worth paying, and how you know the system is still behaving next month.
Most teams are making those calls for the first time, with no one outside the team who has made them in a different context. The patterns are still moving, and the trade-offs depend on your data, your users, your cost limits, and how much risk your organization will accept.
Across five weeks, work through those decisions with senior engineers from other companies, then apply the thinking to AI systems you are already building or running.
Full syllabus
Becoming an AI-Native Engineering Team
Define what AI-native engineering means for your team today: product thinking, daily habits, architectural trade-offs, and where AI adds value beyond the hype. Draw on QCon material from Hilary Mason on AI ambiguity and Naomi Saphra on why language models continue patterns rather than reason. Assess where your team sits on the maturity scale, discuss how to keep existing risk and resilience practices as AI comes in, then map your current SDLC, set one boundary where AI stays out, and define a measurable signal for success.
Designing & Building RAG & Context Pipelines
Go past prompt engineering to design production RAG systems and context engineering pipelines, where quality depends on data, retrieval design, and memory. Find where traditional RAG breaks in production, compare vector RAG with Graph RAG across contrasting use cases, and separate ephemeral context from durable knowledge. Then design a retrieval architecture for a complex query scenario in your organization, justify each layer, and name the failure mode you expect most.
Designing & Building AI Agents
Design agentic systems that hold up beyond the demo, covering orchestration, control, memory, and the trade-off between autonomy and certainty. Critique a real agent architecture for failure modes such as tool errors and runaway loops, and test the criteria for when one well-tooled agent beats multi-agent orchestration. Then design an agent for a real task, with its tool inventory, expected failure modes, escape hatches, and an evaluation plan.
AI Platforms & Infrastructure
Work through the platform and infrastructure patterns that make AI systems production-ready, including inference gateways, GPU cost controls, and what to centralize versus federate. Review a system architecture for inference waste, defend the stack choices that reduce token costs, and sketch the gateway policies (rate limits, cost caps, PII handling) your organization needs. Then outline an internal AI platform strategy that covers workload routing for latency-critical tasks and the core observability signals for system health.
AI Operational Excellence: Evals, Trust & Reliability
Examine evaluation metrics, trust, and rollout readiness as the operational disciplines behind dependable AI systems. Most of the session is capstone presentations and peer discussion.
Get the cohort details in your inbox.
We’ll send you the schedule, facilitator, full syllabus, weekly time commitment, price, registration link, and manager approval page in one email.
A live certification cohort, not another self-paced course.
Selected QCon and InfoQ technical material provides a starting point. The live sessions are where the group examines trade-offs, challenges assumptions, and sees how the same issue changes in other environments.
Between sessions, put the thinking into practice against systems, proposals, controls, assessments, or decisions you already own.
Hien stays with the cohort across the five weeks so later discussions can build on what participants tried between sessions.
How the five weeks work
Compare where the same control changes.
A control that works in one environment can behave differently when the data, architecture, regulation, threat model, team structure, or operating constraints change.
The cohort gives you other reference points while the decision is still current. The value is not generic networking. It is seeing where an approach transfers, where it changes, and why.
Finish with an assessment in AI Engineering Program you can point to.
Develop a documented risk assessment and mitigation report for an AI product architecture and present the work as part of the cohort.
Complete the current attendance, participation, and capstone requirements to earn the InfoQ Certified AI Engineering Program credential.
See the kind of work the capstone produces.
The capstone is developed as a professional technical article built around a documented risk assessment and mitigation report for an AI product architecture. Selected work may be published on InfoQ under the current program terms.
Is this the right cohort for the work you own?
Designed for experienced practitioners, generally with at least five years of professional experience.
Start from QCon and InfoQ technical material.
Selected QCon talks and InfoQ technical material provide the starting point for the cohort. The live sessions are where participants examine those ideas against different systems, organizations, and constraints.
Open the shared Convince Your Manager page with this cohort preselected. The guided prompt uses the current October 14 intake facts and asks about your role, current work, the problem you want to work through, what your manager cares about, and the most likely objection.
It then drafts a ready-to-send manager email, a version under 150 words for Slack or Teams, five short talking points, and one note on any missing detail that would materially strengthen the request.
Cohort questions
Building AI systems that hold up in production.
with Hien Luu
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