Agentic AI & Production LLM Systems
A 16-week advanced program for practicing engineers & professionals.
Become a production-grade GenAI and agentic AI practitioner, with the depth to lead AI initiatives rather than just contribute to them.
Weeks
16
Training hours
144
Hands-on labs
~72%
Enterprise capstones
10+
Who it’s for
Working software engineers, data scientists, and technical professionals who already know how to build software and want to become production-grade GenAI and agentic AI practitioners, with the depth to lead AI initiatives rather than just contribute to them.
Why this course
No beginner content. We assume you can already code and reason about systems: every week goes straight into applied, production-grade GenAI engineering.
Same 16-week depth as our Freshers program, but every week is recalibrated for experienced engineers: faster ramp-up, more advanced scenarios, and enterprise-scale concerns from day one.
Two full orchestration tracks: learn both workflow-automation platforms (n8n-style, for business process automation) and multi-agent frameworks (CrewAI), the two dominant patterns employers are hiring for right now.
Goes beyond individual-contributor skills into scaling multi-agent systems, cost governance, compliance, and the stakeholder communication needed to lead AI initiatives: content the Freshers program doesn’t cover.
Every module maps directly to a named, enterprise-grade capstone scenario (Tier-1 Resolution Agents, AP Automation, Renewal Risk Agents, Multi-Agent Research-to-Brief Factories), not toy examples.
What you’ll build
Two reliability-focused mini-apps in week one alone: a constrained brief generator and a policy-driven support reply drafter.
A production-grade RAG system, iterated from v1 to v2, with a formal evaluation harness measuring faithfulness, retrieval hit-rate, latency, and cost.
A multi-tool agent built on the Model Context Protocol, extended into a full multi-agent workflow using either n8n-style automation or CrewAI, then scaled with agent-to-agent communication and shared memory architectures.
An enterprise-grade capstone of your choice, spanning both GenAI/RAG builds and autonomous agentic systems, with a business case, cost governance model, security hardening, and a defense in front of a simulated architecture review board.
Curriculum highlights
Weeks 1 to 2
Advanced prompt engineering and failure-mode mitigation, plus provider strategy and cost-aware model selection.
Weeks 3 to 5
Production-grade RAG (built and optimized twice) and a formal LLM evaluation and observability harness.
Weeks 6 to 7
Agentic AI core architecture (MCP, multi-agent orchestration) and hands-on workflow automation using n8n and CrewAI.
Weeks 8 to 10
A real enterprise capstone: product framing with a business case, fine-tuning and multimodal extensions, and scaling into advanced multi-agent orchestration with shared memory.
Weeks 11 to 13
Production integration and cost governance, security/red-teaming and compliance, and LLMOps/release engineering at scale.
Weeks 14 to 16
Enterprise AI strategy and stakeholder communication, a simulated architecture review board, and a final showcase with technical viva.
THE OUTCOME
Which course is right for you?
| Freshers | Professionals | |
|---|---|---|
| Duration | 16 weeks / 144 hours | 16 weeks / 144 hours |
| Prior AI experience needed | None | Software engineering background; no AI experience required |
| Pace | Steady, foundational build-up | Fast ramp-up, sustained advanced depth throughout |
| Prompting & GenAI basics | Full module, from scratch | Brief refresher in Week 1, then straight to advanced patterns |
| RAG & Agentic AI depth | Full depth, iterative build (v1 to v2) | Same iterative depth, plus scaling to multi-agent, shared memory |
| Orchestration tools | MCP + intro to n8n/CrewAI | MCP + full n8n and CrewAI tracks, plus multi-agent scaling |
| Enterprise strategy & leadership | Not covered | Dedicated weeks on business case building, vendor evaluation, cost governance, and stakeholder communication |
| Career support | Resume, LinkedIn, mock interviews included | Specialization pathways (AI Architect, Staff AI Engineer) and architecture review board simulation |
| Best for | Graduating engineers entering the job market | Working professionals upskilling into senior/lead AI roles |
Graduating this year? See GenAI Engineering Foundations to Production, the Freshers program.
Go beyond prompts. Build real AI systems.
Whether you’re graduating and starting your career, or already building software and ready to specialize in AI, there’s a direct path from where you are to shipping real, production-grade GenAI and agentic AI systems.
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