Course Category
Google Rating
Professionals Upskilled
Live Cohorts Delivered
Enterprise Teams Trained
Solution Architects are no longer only designing traditional applications. They are increasingly expected to make decisions around RAG pipelines, AI agents, LLMOps, security, cost, and enterprise integration while still balancing business requirements, scalability, and stakeholder expectations.
This certification helps experienced architects make that shift. You will learn to architect AI systems, evaluate vendors, design production-ready architectures, manage security and cost, integrate AI with legacy systems, and get your architecture approved and adopted.
Design Production-Grade RAG - Make defensible decisions across chunking, embeddings, vector databases, hybrid search, and retrieval accuracy.
Architect Multi-Agent Systems - Choose the right orchestration pattern, tool-use approach, and human-in-the-loop controls.
Design for Production - Build LLMOps architectures with model serving, versioning, rollback, monitoring, and retraining strategies.
Control AI Costs Before Production - Build FinOps models using token economics, caching, model tiering, and inference forecasting.
Prepare for AI Security Reviews - Address prompt injection, tool misuse, data leakage, PII, and AI threat modeling before deployment.
Integrate AI With Legacy Systems - Add RAG and agents to existing systems without proposing an expensive rewrite.
Accelerate Architecture Work - Use AI to generate architecture documents, RFP responses, vendor comparisons, and executive summaries.
Build an Architecture Stakeholders Can Approve - Build governance and rollout plans that help technical and business stakeholders trust and adopt your solution.
OUTCOME
Make a defensible vendor choice, not just “use whichever tool”.
OUTCOME
Generate client-ready architecture docs and RFP responses in a fraction of the time.
TIME SAVED
~40 hrs → 8 hrs per RFP (80% faster)
OUTCOME
Design a production-grade RAG pipeline with defensible accuracy-vs-latency trade-offs.
TIME SAVED
~3-week cycle → 3–4 days (~85% faster)
OUTCOME
Architect a multi-agent system with the right orchestration pattern and safety
checkpoints.
TIME SAVED
~2-week prototype → 2–3 days (~80% faster)
OUTCOME
Design deployment architecture that survives beyond a demo, with monitoring built in.
TIME SAVED
~2-week planning → 3–4 days (~75% faster)
OUTCOME
Build a working cost model before a system ships, instead of discovering the bill in production.
TIME SAVED
~1 week guesswork → 1 hr with a model
OUTCOME
Answer the questions that currently stall AI projects at the architecture-review stage.
TIME SAVED
Avoids 4–6 weeks of security rework
OUTCOME
Integrate AI into a 10-year-old system without proposing a rebuild the business won’t approve.
TIME SAVED
Rebuild proposal → 4–6 week integration plan
OUTCOME
Leave with a rollout and adoption plan your team can actually run not just a technically correct design.
TIME SAVED
~12 hrs debate → 2–3 hrs with a playbook
I’ve been designing systems for 15 years, but the RAG and multi-agent modules genuinely changed how I approach a new engagement. The FinOps module alone caught a cost issue I would have missed until production.
Most AI courses for architects stay theoretical. This one made me build a real security threat model for agentic systems, which I used in an actual client review two weeks later.
The legacy integration module was the one I needed most every AI course assumes greenfield, but my actual job is bolting this onto a 12-year-old core system.
Many architects on technical communities initially felt AI was “just another tool.” Most changed their view after using it for requirements analysis, ADRs, trade-off analysis, and design reviews where hours of work became minutes.
No. AI can suggest options and identify patterns, but business judgment, trade-offs, and stakeholder decisions still require experienced architects. This course is built to make you faster, not replace your judgment.
No. The focus is on architecture thinking, decision-making, system design, and AI-assisted workflows.
Yes, and it’s weighted toward architecting: roughly 2 hours on using AI to accelerate your existing work, and 9+ hours on designing RAG pipelines, multi-agent systems, LLMOps, security, and cost architecture — matching what 2026 job postings actually require.
No. A dedicated module covers integrating AI into existing legacy systems and monoliths, since most real enterprise AI work is brownfield, not greenfield.
A complete architecture package covering design, security, cost, and rollout for a real use case from your own company, presented as your capstone.
