Applied AI Engineering
Applied AI Engineering
If you work in IT — as a developer, tester, support engineer, or data engineer — and AI is reshaping everything around you with no clear path to get hands on it, this is where you start. Not with slides about AI. With a running agent, in your field, built by you, in Session 1.
No data science background needed. No ML theory. You bring your IT experience; the course gives you the tools, the structure, and the hands-on build time to use them.
What You’ll Learn
- Two deployed AI applications with public URLs, both on GitHub, both yours
- Adopt lean and AHands-on engineering experience with LangGraph, MCP, LangSmith, and LlamaGuardgile processes for faster delivery
- One built live in class alongside the instructor. One built independently across your weekly assignments.
- A live Demo Day presentation to the full batch
- Post-course reference pack — cheat-sheets, agent starter templates, prompt engineering guide, and debugging runbook
Course Benefits
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Who can attend this course
Developers
Operations
Testers
- All our trainers are having minimum 10 years of experience in test automation.
- Every session we conduct is a combination of theory and hands-on.
- All sessions are recorded which participants can keep with them for life time.
OUR VALUABLE
Courses Benefits
Enhance your skills with our diverse range of software testing courses and become a proficient tester in the dynamic IT industry.
Detailed Course Content
Optional Pre-Course
- Variables
- functions
- loops
- pip
- virtual environments
- python-dotenv, requests, and pydantic
Foundations
- AI Engineering Mindset + Coding
- LLMs + Context Engineering
- LangGraph Foundations
- Memory + RAG
- Vector DBs, ChromaDB, retrieval quality, hybrid search and reranker awareness, grounding responses in real documents
- Nodes, edges, state, conditional routing, multi-turn context
- How LLMs work, context engineering (context window strategy, instruction hierarchy, grounding), structured outputs, Pydantic models, responsible output design — grounding as the foundation of auditability
Agent Capabilities
- Function calling, tool design patterns, connecting agents to REST APIs
- Model Context Protocol architecture, local MCP server, STDIO transport, MCP Inspector
- Advanced MCP patterns, connecting MCP tools inside LangGraph agents, multi-tool orchestration
- ReAct pattern, critique-revise loops, core reliability patterns (retries, fallbacks, confidence scoring), human-in-the-loop and escalation design — auditability and traceability as first-class reliability properties
- LangSmith tracing, evals, cost & latency optimization, token budgeting, testing non-deterministic outputs, reading traces to find and fix failures
- LangGraph supervisor pattern as an architectural introduction — when to split into specialist agents vs. when to keep one agent, designing agent responsibilities and handoffs, LangGraph vs. CrewAI tradeoffs (30 min)
Industry Perspectives
- An invited AI engineering practitioner walks through a real deployment: architecture decisions made, what failed, what scaled, team structure, and what skills matter most in 2026. Q&A-led — participants bring their own questions.t
Architecture Decisions + Agent UI
- Architecture decision-making (agent vs. workflow, single vs. multi-agent, when simple Python beats a five-agent system), Streamlit interface design for agent applications, session state and conversation management in UI
Containerisation + Cloud Deployment
- Docker containerisation, environment variable management, secrets handling, and deploying a containerised agent to the cloud — participants use whichever cloud their organisation already runs
Security & Compliance
- LlamaGuard, OWASP LLM Top 10, DPDP Act 2023, prompt injection defences, audit trails — building on the grounding and traceability patterns introduced in earlier sessions
Agent Testing & Quality
- Testing non-deterministic agents, golden dataset design, eval suites in LangSmith, LLM-as-judge eval patterns — using a second LLM to score your agent's outputs at scale, quality gates before production
Demo Day
- Public showcase — deployed Launchpad presented live to invited industry professionals: practising AI engineers, mentors, and hiring managers from partner organisations
Frequently Asked Questions
1.Do I need to know Python well?
Basic Python is enough — variables, functions, loops, installing packages. And because you build using an AI coding assistant throughout (Claude Code or GitHub Copilot), you won't be writing implementation code from scratch — you'll be directing the AI, reading what it produces, and understanding the architecture. Session 0 (optional, 2.5 hours) covers Python fundamentals plus the three libraries used throughout the course: python-dotenv, requests, and pydantic. Attend if you need it, skip it if you don't.
2.What are the two apps I actually build?
WealthDesk is built in class alongside the instructor session by session: you see every architectural decision made live. Your Launchpad is built independently through weekly assignments in your chosen domain: ClinicalIQ (Healthcare), QuickLoan (Banking), or BundleIQ (Telecom). Both are deployed with public URLs on Streamlit Community Cloud and containerised with Docker.
3.What does it cost beyond the course fee?
LLM API costs stay under ₹500 for the full course — Groq's free tier and Ollama cover this at no charge. The one paid subscription is your AI coding assistant: Claude or GitHub Copilot at approximately ₹2,000/month. Total tooling spend for the duration: under ₹2,500/month. We walk through every setup together in Session 1.
4.What if I miss a session?
Recordings go up within 24 hours. An optional Thursday drop-in (1 hour, on confirmation) is available if you're stuck on an assignment before the next session. Each session builds on the last. Staying current matters, and we actively help you do that. The Launchpad assignment structure means missing one session doesn't break your build; you can catch up before the next one.
5.Why LangGraph and not other frameworks?
LangGraph gives you stateful, structured agent graphs — the kind that can be traced, debugged, and deployed. We include a 30-minute CrewAI comparison in Session 10 so you understand the trade-offs. LangGraph goes deep. The comparison gives you the vocabulary to evaluate any other framework yourself.
6.Is there a certificate?
Yes, a course completion certificate. More practically, you'll have two deployed applications on GitHub with live public URLs. In a technical conversation, those applications will do considerably more work for you than a certificate.
7.How deep does 40 hours actually take me?
Deeply enough to build and deploy two applications. Not toy demos. By Session 15, both WealthDesk and your Launchpad are containerised with Docker, live at a public URL, secured against the OWASP LLM Top 10, fully traced in LangSmith, and tested with a golden eval suite. The breadth is wide by design: the programme covers the full engineering lifecycle so you can own a deployed system end to end. Some areas (like advanced multi-agent choreography or ML fine-tuning) are architectural introductions, not deep dives. The goal is a working, deployed system with every layer understood, not a research-level mastery of each component.
That outcome requires effort beyond the 40 live hours. Each session comes with 15–20 minutes of pre-reading via WhatsApp. Each session has a follow-up assignment — ~2 hours in early sessions, ~3–4 hours in the later phases — where you apply what was covered to your own agent independently. The live sessions give you the concepts and live demonstration; the assignments are where the learning locks in. If you show up to sessions but skip the assignments, you'll follow along but won't ship.
That outcome requires effort beyond the 40 live hours. Each session comes with 15–20 minutes of pre-reading via WhatsApp. Each session has a follow-up assignment — ~2 hours in early sessions, ~3–4 hours in the later phases — where you apply what was covered to your own agent independently. The live sessions give you the concepts and live demonstration; the assignments are where the learning locks in. If you show up to sessions but skip the assignments, you'll follow along but won't ship.
8.How much do I need to code manually?
Less than you might expect. From Session 1, you build using an AI coding assistant (Claude Code or GitHub Copilot). The assistant writes the implementation; you direct it, review the output, understand what it's doing, and make it fit your agent's needs. Python in this programme is for reading and following AI-generated code, not writing everything from scratch. Session 0 (optional, 2.5 hours) covers the Python basics you need to stay oriented. The skill you're building is knowing what to ask the AI to build, spotting when it goes wrong, and understanding the architecture well enough to correct course. That's what transfers to the job.
9.Is this worth it compared to self-learning?
Most people who try to learn AI engineering independently spend 6–12 months across fragmented tutorials, deprecated docs, and courses that never connect into something deployable. This programme compresses that into a structured 16-session sprint: curriculum reviewed by practising AI engineers, live expert guest sessions, two apps you actually deploy, and a Demo Day where you present to industry professionals.
Our Refund Policy
Know Your Refund Benefits
1. Cancellation Window
Cancellations must be made within two weeks from the start date of the training program to be eligible for a full refund.
2. Refund Eligibility
No refunds will be issued for cancellations requested after the two-week period has elapsed.
3. Processing Time
All approved refund requests will be processed, and payments will be made within 10 business days from the date of acknowledgment of the request.
