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AI Engineering Master Program

Build and deploy production-grade AI systems. Master MLOps, Vector Databases, RAG pipelines, model deployment and AI infrastructure with IBM certification.

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Duration

9 Months

Mode

Live Online

Certified by

IBM + IIT Patna

Live Online

9 Months

Learnbay

94% placed

Placement Rate 94%

Alumni 15,000+

Rohit Das AI Infrastructure Lead @ Zomato

"MLOps and model deployment skills are incredibly rare. This program made me the most hireable AI engineer in my batch."

Nisha Reddy ML Platform Engineer @ PhonePe

"The Vector DB and RAG pipeline projects are exactly what production AI systems need. Brilliant curriculum."

What You'll Learn

Program Curriculum

Seven intensive terms from Python and ML foundations through LLMs, Agentic AI, AI system architecture, and production LLMOps — everything an AI engineer needs to build and ship at scale.

Python: OOP, decorators, generators, async programming

NumPy, Pandas & scientific computing libraries

Working with REST APIs, JSON & data pipelines

ML foundations: supervised, unsupervised, evaluation

Scikit-learn: training, tuning & cross-validation

Git, GitHub & Python project structure best practices

Transformer architecture: attention, positional encoding

Pre-training, RLHF & instruction tuning

Tokenisation, text embeddings & semantic search

Prompt Engineering: system prompts, few-shot, CoT

OpenAI API, Anthropic API & IBM Watsonx

LLM evaluation: benchmarks, BLEU, ROUGE, human eval

LangChain: chains, memory, tools & document loaders

Retrieval-Augmented Generation (RAG) end-to-end

Vector databases: Pinecone, ChromaDB, Weaviate, FAISS

Advanced RAG: re-ranking, hybrid search, parent-child chunking

LLM fine-tuning: PEFT, LoRA, QLoRA, full fine-tuning

Hugging Face: model hub, training API, inference endpoints

Multimodal models: vision-language, audio-text pipelines

Agent design patterns: ReAct, CoT, ToT, self-reflection

LangGraph: stateful agent workflows & conditional routing

AutoGen: multi-agent collaboration & conversation design

Tool use: function calling, code execution, web search

Memory systems: short-term, long-term, episodic memory

Building production autonomous agents with guardrails

Designing scalable AI inference architectures

Microservices for AI: FastAPI, gRPC, event-driven patterns

GPU infrastructure: CUDA, model parallelism, batching

AI caching strategies: semantic caching, response memoisation

Streaming LLM responses: SSE, WebSockets, token streaming

AI gateway patterns: routing, rate limiting, cost controls

LLMOps lifecycle: experiment tracking, versioning, registry

Model serving: vLLM, TGI, BentoML, Triton

Docker & Kubernetes for AI workloads at scale

CI/CD for AI: model testing, staging, canary deployment

Production monitoring: latency, throughput, cost, drift

Guardrails: output validation, toxicity filtering, PII redaction

Production RAG system: document Q&A at enterprise scale

Multi-agent research & report generation platform

LLM fine-tuning pipeline for domain-specific tasks

AI microservice with full monitoring & observability

AI Co-Lab: solve a real startup problem, earn a certificate

BYOP: bring your own project with dedicated mentorship

Tools & Technologies

What You'll Work With

Python LangChain LangGraph Hugging Face OpenAI API IBM Watsonx Pinecone ChromaDB Weaviate FAISS AutoGen FastAPI vLLM Docker Kubernetes AWS SageMaker Azure ML MLflow Prometheus Grafana Scikit-learn TensorFlow PyTorch Git & GitHub

Ideal Candidates

Who Should Enrol

Software Engineers Pivoting to AI

Backend or full-stack developers with Python knowledge who want to specialise in building and operating production AI systems and LLM applications.

ML Engineers & Data Scientists

ML practitioners who want to advance into AI engineering — moving beyond model training into production deployment, MLOps, and LLM system design.

Platform & DevOps Engineers

Infrastructure engineers who want to build AI platforms and LLMOps pipelines — bringing DevOps rigour to AI model deployment and monitoring.

Working Professionals (1–7 Years)

IT professionals with 1 to 7 years of experience and basic Python knowledge who want to move into AI engineering roles at product companies and AI startups.

Credentials

Certifications You'll Earn

IBM

IBM GenAI Engineering Certificate

Globally recognised IBM credential validating your LLM engineering, RAG, and AI deployment skills — highly valued at Fortune 500 companies and AI-first product firms.

IIT Patna

IIT Patna Collaboration Certificate

Academic credibility from India's premier IIT — a strong differentiator for senior AI engineering roles and adds prestige to your professional profile.

Microsoft Azure

Azure AI Fundamentals Certificate

Microsoft-backed certification validating cloud AI capabilities on Azure — a top requirement for AI engineering roles in enterprise and consulting organisations.

AI Co-Lab

Startup AI Engineering Project Certificate

Employer-endorsed certificate from solving a real AI engineering challenge for a live startup — tangible portfolio evidence of production-grade AI system skills.

Career Services

Complete Placement Support

Resume Building

AI-engineer-focused resumes highlighting LLM systems, RAG pipelines, and MLOps experience — tuned for top AI product companies and startups.

Mock Interviews

AI engineering technical interviews: LLM system design, RAG architecture, MLOps pipelines, and coding challenges at FAANG and AI startup formats.

Interview Referrals

Direct interview calls at 350+ partner companies including Amazon, Microsoft, Meta, Paytm, and leading AI-first startups across India and globally.

LinkedIn Optimisation

Profile keyword strategy for AI Engineer, LLM Engineer, and ML Platform Engineer roles — getting discovered by the right tech recruiters.

24/7 Doubt Support

Around-the-clock support for debugging AI systems, architecture questions, and guidance on project implementation challenges.

1-on-1 Mentorship

Personalised career guidance from AI engineers and platform leads at Amazon, Microsoft, and top AI-first product companies.

Investment

Program Fee & Enrolment

₹1,59,000

+ 18% GST | EMI from ₹13,250/month

300+ hours of live instructor-led classes
3-year subscription with unlimited access
15+ guided AI engineering projects
IBM + IIT Patna + Azure certifications
AI Co-Lab startup collaboration
Complete placement assistance
24/7 doubt resolution support

Enroll Now → Book Free Demo Class

No-cost EMI available on all major bank cards & NBFC partners

FAQ

Frequently Asked Questions

Basic Python knowledge and familiarity with programming concepts are recommended. Some exposure to ML or data science is a plus but not mandatory — the program starts with a thorough Python and ML foundations term. Candidates from 0 to 7 years of experience are accepted.

The AI Engineering Master Program goes deeper into production infrastructure — AI system architecture, platform engineering, model serving (vLLM, TGI), and ML foundations including scikit-learn and PyTorch. It is designed for engineers who want to build the AI stack end-to-end, not just the application layer.

You will be qualified for AI Engineer, LLM Engineer, ML Engineer, AI Platform Engineer, and MLOps Engineer roles. These are among the highest-demand and highest-compensated engineering positions at product companies, AI startups, and enterprise tech firms in 2025.

Yes. You build 15+ projects across the program including a production RAG system, fine-tuned domain LLM, multi-agent platform, and an AI microservice with full monitoring. The AI Co-Lab capstone is a real startup project that produces an employer-endorsed certificate.