Master Agentic AI
Build, Deploy, and Operate Production AI Systems
Modules
Cloud Platforms
Weeks
Subsystems
The AI Platform Engineering Gap
Everyone is using AI tools. But who builds the platforms that power them? RAG pipelines, agentic systems, fine-tuned models, guardrails, evaluation frameworks, and multi-cloud deployments require a different skill set — one the market is only beginning to develop. Most engineers can call an API. Few can architect and operate the full AI engineering stack.
"The real skill gap isn't using AI tools. It's building and running the AI platforms that power them."
Industry data shows AI/ML platform engineering roles have grown 3x since 2023, with demand outpacing the supply of engineers who can deploy, evaluate, and monitor production AI systems at scale.
Build SageDesk — An Enterprise AI Assistant
A production-grade, multi-tenant AI assistant with RAG, agents, fine-tuning, guardrails, evaluation, ML pipelines, and multi-cloud deployment — built incrementally across all nine modules.
10 Systems Built
10 fully integrated subsystems across the AI platform engineering lifecycle
4 Cloud Platforms
Vertex AI, Bedrock, Azure AI Studio, and OpenShift AI — all four deployed
75+ Technologies
LangChain, LlamaIndex, MLflow, Langfuse, Prometheus, vLLM, and more
SageDesk Subsystems You Build
| Subsystem | What It Does |
|---|---|
| Knowledge Ingestion | Upload, chunk, embed, and store company documents for retrieval |
| RAG Pipeline | Retrieve, rerank, and generate grounded answers with source citations |
| Agent Orchestration | Multi-agent system — search, action, and escalation agents with tool use |
| Model Router | Route simple queries to SLMs, complex to LLMs — optimize cost and quality |
| Fine-Tuning Pipeline | Prepare training data, run LoRA/QLoRA, version and promote models |
| Guardrails Engine | PII detection, content filtering, hallucination detection |
| Evaluation Framework | Automated RAGAS metrics, A/B testing between model versions |
| ML Analytics Pipeline | Usage analytics, topic clustering, satisfaction prediction with MLflow |
| Platform Monitoring | Latency tracking, drift detection, auto-scaling, anomaly alerting |
| Multi-Cloud Deployment | Deploy on Vertex AI, Bedrock, Azure AI Studio, and OpenShift AI |
Technologies Used
Who Should Attend
This program is designed for engineers who want to architect, build, and operate production AI platforms — not just consume AI APIs.
ML Engineers
Move beyond model training — learn to deploy, serve, evaluate, and monitor in production
Platform Engineers
Add AI infrastructure to your toolkit — model serving, GPU management, auto-scaling
Backend Developers
Transition into AI engineering — build RAG pipelines, agents, and LLM applications
Solutions Architects
Evaluate cloud AI platforms with hands-on deployment experience across all four
DevOps Engineers
Extend CI/CD to ML models, implement LLMOps and AIOps practices
Engineering Managers
Understand what it takes to build and operate AI platforms — make informed team decisions
Four Modules. 16 Weeks. One Complete Platform.
Each module extends SageDesk with new capabilities. By the end, you have built a production-grade AI platform from scratch.
Four Cloud Platforms. Complete Deployment.
Every student deploys SageDesk on all four platforms — not just one. You leave with first-hand experience of each and the ability to make informed platform recommendations.
Managed model endpoints, Vertex AI Search, Vector Search, Cloud Monitoring
Serverless model access, Knowledge Bases, Bedrock Agents, Bedrock Guardrails
Azure OpenAI, AI Search, Content Safety, Prompt Flow
Self-managed model serving (KServe/vLLM), Kubernetes-native, full control
Module 5 dedicates 2 weeks to each platform. You understand the trade-offs between managed serverless (Bedrock), fully managed (Vertex AI, Azure AI Studio), and self-managed Kubernetes-native (OpenShift AI).
What Makes This Program Different
All Four Cloud Platforms, Not One
Most programs teach one cloud. You deploy on all four and understand the trade-offs — managed vs. self-hosted, serverless vs. Kubernetes, proprietary vs. open-source.
End-to-End, Not Just Models
From document ingestion to production monitoring. RAG, agents, fine-tuning, guardrails, evaluation, MLOps, AIOps — the complete AI platform engineering lifecycle.
One Real Project Throughout
SageDesk grows with each module. Every concept has a concrete, working implementation — not isolated exercises that go nowhere.
Production Practices from Week 1
Cost tracking, tenant isolation, model versioning, guardrails, evaluation metrics, drift detection — production concerns are woven into every module, not bolted on at the end.
Open-Source First
Local development with Ollama and open-source models. MLflow and Langfuse are self-hosted. No vendor lock-in — you own every piece of the stack.
Learning Outcomes
Upon completing this program, participants will be able to:
- Design and implement RAG pipelines with retrieval, reranking, and grounded generation
- Build multi-agent systems with tool use, memory, and orchestration
- Fine-tune open-source models using LoRA/QLoRA and manage model versions
- Deploy AI applications on Vertex AI, Bedrock, Azure AI Studio, and OpenShift AI
- Implement guardrails for PII detection, content filtering, and hallucination detection
- Build evaluation pipelines with RAGAS metrics and A/B testing
- Track ML experiments with MLflow and implement CI/CD for models
- Monitor AI systems with latency tracking, drift detection, and auto-scaling
- Route queries between SLMs and LLMs for cost-optimized inference
- Make informed cloud AI platform recommendations based on hands-on experience
Program Schedule
- Format
- Instructor-led, hands-on labs
- Delivery
- Virtual (live, instructor-led)
- Duration
- 16 weeks (one session per week)
- Session
- Every Sunday, 2 hours
- Lab environment
- Personal laptop (Ollama for local models) or cloud-based lab instances
- Languages
- Python 3.14 (backend) · TypeScript 5.8 / React 19 (frontend)
- Takeaways
- All lab code, deployment configs, and reference guide
Session Time by Timezone
| Region | Timezone | Session Time |
|---|---|---|
| India | IST (UTC+5:30) | Sunday 7:00 PM – 9:00 PM |
| USA (East Coast) | EST (UTC-5) | Sunday 8:30 AM – 10:30 AM |
| UK / Europe | GMT (UTC+0) | Sunday 1:30 PM – 3:30 PM |
| UAE / Middle East | GST (UTC+4) | Sunday 5:30 PM – 7:30 PM |
| Singapore / East Asia | SGT (UTC+8) | Sunday 9:30 PM – 11:30 PM |
Simple, Transparent Pricing
One-time fee. No hidden charges. Full program access from day one.
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Master Agentic AI
one-time payment
- 75+ technologies across 9 modules
- Deploy on all 4 cloud AI platforms
- Build SageDesk — an enterprise AI assistant
- Sunday live sessions
- Certificate on completion
Ready to Build AI Platforms?
Join the next cohort — Sunday sessions, open-source tools, one real enterprise AI assistant.
Rathinam Trainers & Consultants Private Limited
Local development uses Ollama and open-source models. MLflow and Langfuse are self-hosted. No vendor lock-in.