Tech IT SolutionAI Engineering
Build production-grade AI systems — LLMs, RAG, Agents, Fine-tuning, MLOps, Deployment & Scalable AI Infrastructure for real-world enterprise applications in 2026
Why join this course
AI Engineers are among the highest-demand roles in tech. This program takes you beyond using AI tools to building production-grade LLM applications: RAG systems, fine-tuned models, multi-agent workflows and cloud deployments, with real enterprise-style projects.
Build real LLM products
Ship retrieval, agent and fine-tuning projects, not toy demos.
Deploy on the cloud
Work with AWS Bedrock, Azure AI, Vertex AI, Docker and Kubernetes.
Production mindset
Learn monitoring, evaluation, versioning and CI/CD for AI systems.
Future-proof career
Skills employers hire for in AI, ML and automation teams.
By the end you will be able to
- Advanced LLM Architecture & Prompt Engineering
- RAG Systems, Vector Databases & Retrieval Engineering
- Fine-tuning, PEFT (LoRA, QLoRA), Model Quantization & Evaluation
- Agentic AI Frameworks (LangGraph, CrewAI, AutoGen)
- MLOps & LLMOps: Monitoring, Versioning, CI/CD for AI
- Cloud Deployment (AWS Bedrock, Azure AI, GCP Vertex AI, Docker/K8s)
- 100% Hands-on + Production-grade Projects + Certificate
Who is this course for?
Add a job-ready skill next to your degree or diploma.
Upskill for a promotion, a better role or a career switch.
Use these skills to deliver client work or run your own business better.
Curriculum overview
6 modules, explained in detail. Open a module to see exactly what is inside.
1Foundations of Modern AI Engineering7 topics
Understand how modern LLMs really work, so you can debug, tune and choose models with confidence instead of guessing.
What is inside
- Transformer Architecture Deep Dive
- LLM Training vs Inference
- Tokenization
- Attention Mechanisms
- Open vs Closed Models
- Prompt Engineering Advanced
- Evaluation Metrics (BLEU, ROUGE, BERTScore, Human Eval)
2Retrieval-Augmented Generation (RAG) Engineering6 topics
Ground language models in your own data. You will design retrieval pipelines that give accurate, source-backed answers.
What is inside
- Vector Databases (Pinecone, Weaviate, Chroma, Qdrant)
- Embeddings (OpenAI, Cohere, Hugging Face)
- Hybrid Search
- Re-ranking
- Chunking Strategies
- Advanced RAG Patterns (HyDE, Self-RAG, Corrective RAG)
3Fine-tuning & Model Optimization6 topics
Adapt models to your domain efficiently. Learn when to fine-tune, how PEFT methods save cost, and how to measure improvement.
What is inside
- Full Fine-tuning vs PEFT (LoRA, QLoRA, DoRA)
- Quantization (4-bit, 8-bit)
- Model Distillation
- Instruction Tuning
- Preference Optimization (DPO, PPO)
- Evaluation Frameworks (LM-Eval, Open LLM Leaderboard)
4Agentic & Multi-Agent Systems Engineering10 topics
Move from single prompts to systems of cooperating agents that plan, use tools, remember context and check each other.
What is inside
- LangGraph
- CrewAI
- AutoGen
- LlamaIndex Workflows
- Memory Systems
- Tool Calling
- Reflection & Planning
- Multi-Agent Collaboration
- Supervisor + Worker Patterns
- Dynamic Routing
5MLOps / LLMOps & Production Systems11 topics
Running AI in production needs tracking, monitoring and repeatable releases. This module covers the MLOps/LLMOps toolchain.
What is inside
- MLflow
- Weights & Biases
- LangSmith
- PromptLayer
- Phoenix
- Arize
- Grafana + Prometheus for LLM Monitoring
- Rate Limiting
- Cost Optimization
- A/B Testing
- Canary Deployments
6Cloud Deployment & Enterprise AI Engineering + Capstone8 topics
Take your work live on the cloud and finish with an enterprise-style capstone that becomes the centerpiece of your portfolio.
What is inside
- AWS Bedrock
- Azure OpenAI
- Google Vertex AI
- Hugging Face Inference Endpoints
- Docker + Kubernetes
- FastAPI / Gradio / Streamlit Deployment
- Security (PII Detection, Guardrails)
- 3–4 End-to-End Production Projects (RAG Chatbot, Multi-Agent Customer Support, Fine-tuned Domain Model, Enterprise Analytics Agent)
What you will build
Real work for your portfolio, not just exercises.
Enterprise RAG assistant
A document Q&A system with hybrid search, re-ranking and citations.
Fine-tuned domain model
Adapt an open model with LoRA/QLoRA and evaluate it against a baseline.
Multi-agent workflow
A supervisor-and-worker agent system that automates a business process.
Deployed AI service
Containerize, monitor and deploy your model API to the cloud.
Tools you will use
How you will learn
Career outcomes
Roles this course prepares you for:
- Resume & LinkedIn profile building
- Portfolio and project review
- Mock interviews with IT leads
- Hiring-partner drives and referrals
Certificate, batches & details
AI Engineering & LLMOps Specialization
Issued after you pass the final evaluation of the course.
Flexible batches
Morning, afternoon, evening and weekend batches. Classroom at GTB Nagar or live online.
Course at a glance
| Duration | 6–9 months (Fast-track: 4–5 months) |
|---|---|
| Level | Intermediate to Advanced (Python + ML basics required) |
| Mode | Classroom (GTB Nagar, Delhi) & live online |
| Teaching format | Live Sessions + Real Enterprise Projects |
| Prerequisites | Python + ML basics required. |
| Batches | Weekday morning / afternoon / evening & weekend |
| Certificate | AI Engineering & LLMOps Specialization |
| Fees | Call or WhatsApp for the current fee · EMI options available |
Frequently asked questions
Who can join AI Engineering?
Python + ML basics required. Students, graduates, job seekers and working professionals are all welcome.
How long is the course and what are the batch timings?
The course runs for 6–9 months (Fast-track: 4–5 months). We run weekday morning, afternoon and evening batches plus weekend batches for college students and working professionals.
Are classes online or in the classroom?
Both. Attend live in our GTB Nagar (Delhi) classroom or join live instructor-led online classes from anywhere in India. Sessions are recorded, and you can switch between modes.
What certificate will I receive?
AI Engineering & LLMOps Specialization, awarded after you pass the final evaluation.
Will I work on real projects?
Yes. Most of the training is hands-on labs and guided projects, and you finish with work you can show in your portfolio.
What jobs can I apply for after this course?
Typical roles include AI Engineer, LLM Application Developer, MLOps Associate. Your outcome depends on your skills, portfolio and interview performance, and our placement team supports you at every step.
Do you help with placement?
Yes. We help with resume and LinkedIn profiles, portfolio review, mock interviews and hiring-partner drives. This is placement assistance, not a job guarantee.
What is the fee, and is EMI available?
Fees depend on the batch and mode. Call or WhatsApp us for the current fee and flexible EMI / installment options.
Can I attend a free demo class first?
Yes. Book a free demo class to meet the mentors and see how the training works before you enrol.
Related courses
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