Enterprise Gen AI

Course

Enterprise GenAI Engineering

Instructor

Aakash Pandey

Enterprise Gen AI - Future Proof Your Career with Gen AI

Learn to deploy production AI before your job gets automated. 5 weeks of LLMs, RAG systems, and the infrastructure skills that keep you relevant.

5 weeks

Software Engineers to Production AI Engineer

Hybrid (Theory + Hands-on Labs + Live Projects)

Go from software engineer to production AI engineer in 5 weeks. This program teaches you to deploy, optimize, and build production systems with large language models, working hands-on with real enterprise infrastructure.

You won’t just watch tutorials or build toy demos. You’ll deploy open-source models on private servers, build RAG systems that connect to actual enterprise knowledge bases, write Text-to-SQL engines for real ERP databases, and finish with a capstone project that demonstrates you can ship production-ready AI systems.

Duration: 5 Weeks
Level: Software Engineers to Production AI Engineer
Learning Mode: Hybrid (Theory + Hands-on Labs + Live Projects)

What You’ll Master

By the end of this program, you won’t just understand how LLMs work. You’ll know how to deploy them, optimize them, and build systems around them that work in production.

Core Technical Skills:

  • Deploy and optimize open-source LLMs (Llama-3, Mistral, DeepSeek) on private infrastructure
  • Build production-ready RAG systems for enterprise knowledge retrieval
  • Engineer Text-to-SQL systems enabling natural language database querying
  • Fine-tune open-source models using efficient methods like QLoRA
  • Build autonomous AI agents that execute multi-step workflows across systems
  • Design GenAI API architectures with advanced prompt engineering
  • Work with real enterprise data schemas from ERP and CRM systems
  • Manage AI infrastructure with data privacy and compliance in mind

Professional Skills:

  • Production system design balancing performance, cost, and security
  • Technical documentation and architecture diagrams
  • Code review and quality standards at production level
  • Working within enterprise infrastructure constraints and requirements

Prerequisites

This program is built for working software engineers. You need solid technical foundations before you start:

Required: Strong proficiency in Python (functions, classes, async programming)
Required: Working knowledge of SQL and REST API architecture
Beneficial: Familiarity with Linux command line and Docker

If you’re comfortable shipping code in Python and have worked with APIs and databases before, you’re in good shape. If you’re still building those foundations, get them solid first. The program moves fast from day one.

This program is intended for software engineers who are prepared to integrate production-level AI into their current work. It assumes coding proficiency and concentrates solely on constructing practical systems.

Backend Engineers

You already build APIs and work with databases. This program teaches you how to add LLM capabilities to your stack and how to deploy models on your own infrastructure instead of just calling third-party APIs. If you’re comfortable with Python and REST, you’ll feel right at home here.

Data Engineers & Analysts

You work with structured data every day. This program shows you how to add natural language interfaces to your pipelines and build systems that let non-technical users query databases in plain English. You’ll bridge the gap between your data infrastructure and conversational AI.

Software Engineers Transitioning into AI

You can code, but you haven’t worked with LLMs or machine learning yet. This program gives you practical skills to ship AI features without needing a research background. You learn by building systems, not by reading papers. Your software engineering experience is the foundation.

ML Engineers Seeking Production Experience

You understand model theory but haven’t deployed systems in production. This program focuses on the engineering side: serving models efficiently, optimizing inference, and building reliable systems. Less theory, more infrastructure, and real-world constraints.

Final-Year CS Students with Strong Technical Fundamentals

You’ve done well in coursework and have solid programming skills. You’re looking for practical experience beyond what universities teach. If you meet the technical prerequisites and can commit full-time for 5 weeks, this program bridges the gap between academic knowledge and industry practice.

5 modules built around production systems. Each module has hands-on labs using real enterprise infrastructure. You’re building increasingly complex systems as you progress, working with the same setup that production of AI systems run on.

5 Modules

5 Weeks

Expand all sections
Module 1: APIs & Prompt Engineering
1 week

Session 1: LLM Fundamentals & API Integration

  • How LLMs work: tokens, context windows, temperature
  • The stateless nature of AI
  • Lab: Building your first Python client using OpenAI/Anthropic APIs


Session 2: The Art of Prompt Engineering

  • Zero-shot vs few-shot prompting
  • Chain-of-thought reasoning
  • Lab: Writing system prompts that force AI to behave like specific employee roles


Session 3: Building the Interface

  • Introduction to Streamlit/Gradio
  • Managing chat history and session state
  • Lab: Create a corporate chat interface that remembers conversation context
1 week

Session 1: The Open-Source Ecosystem

  • Llama-3, Mistral, and DeepSeek architectures
  • Why privacy and data sovereignty matter in enterprise AI
  • Lab: Introduction to the infrastructure environment (Linux/Docker)


Session 2: Running Local Inference

  • Understanding VRAM and hardware limits
  • Quantization: fitting large models on smaller GPUs
  • Lab: Deploying a model locally using Ollama and vLLM


Session 3: Structured Output Engineering

  • Why JSON is the language of enterprise AI
  • Lab: Forcing a local LLM to output strictly formatted JSON for invoice processing
1 week

Session 1: Vector Databases & Embeddings

  • How computers understand meaning through vectors
  • Setting up ChromaDB and Pgvector
  • Lab: Building a knowledge base from PDF technical manuals


Session 2: Advanced Retrieval Strategies

  • Beyond simple search: hybrid search (keywords + semantic)
  • Re-ranking results for accuracy
  • Lab: Building a legal/HR document search engine


Session 3: Text-to-SQL

  • Connecting AI to structured relational databases
  • Lab: Build a system where users ask questions in English and AI runs SQL queries on an ERP database
1 week

Session 1: Data Preparation

  • Cleaning and formatting corporate data for training
  • Creating instruction datasets in JSONL format


Session 2: Efficient Fine-Tuning with QLoRA

  • Training on consumer hardware using LoRA (Low-Rank Adaptation)
  • Lab: Fine-tuning a Llama model to adopt a specific customer support tone


Session 3: Evaluation

  • Benchmarks vs real-world performance
  • Lab: Testing your fine-tuned model against the base model to prove improvement
1 week

Session 1: Agentic Workflows

  • Introduction to LangGraph/CrewAI
  • Giving AI tools: calculator, web search, database access
  • Lab: Building a recruiter agent that can read a CV and update a database


Session 2: Multi-Modal AI

  • Working with images (vision) and audio
  • Lab: Automated receipt entry system (image to JSON)


Session 3: Capstone Showcase

  • Final project presentation
  • Code review and career mentorship session with engineering leads
Do I need any prior AI or machine learning experience to enroll?
No. The program assumes you’re a software engineer, not a data scientist. You’ll learn what you need to know about how models work, but the focus is on deploying and building with them. If you can code in Python and understand APIs, you’re starting from the right place.
You should be comfortable writing functions, classes, and working with async code. You know how to install packages, set up virtual environments, and debug errors when they come up. If you’ve shipped code in Python professionally or built substantial personal projects, you’re good.
No. You need a decent laptop that can run Python and Docker comfortably, but you don’t need a GPU on your personal machine. You’ll be working on infrastructure that has the necessary hardware. Any modern laptop with 8GB+ RAM and a stable internet connection works fine.
The capstone is a complete AI system you built during the final week. It should demonstrate that you can deploy models, integrate data, and build something production ready. You present it to the engineering team at the end of the program in a code review session. It’s not graded, but it goes into your portfolio and shows what you can actually build.
Most online courses teach you to call third-party APIs. This program teaches you to deploy models yourself, optimize production, and build on private infrastructure with real enterprise constraints. In-person mentorship with actual data schemas, not solo videos and Kaggle datasets. Career paths include BC Consultant, ERP Implementation Specialist, Support Specialist, and BC Trainer in industries like manufacturing, trading, retail, and consulting. Our placement rate is 85% BC. The skills are in high demand.
This program positions you for roles like AI Engineer, ML Engineer (with a production focus), GenAI Engineer, or Backend Engineer with AI capabilities. If you’re already a software engineer, it gives you skills you can bring back to your current role or use to move into a more AI-focused position.
Basic familiarity with Docker is helpful but not required. You’ll learn what you need during the program. Kubernetes isn’t covered in depth because the focus is on building AI systems, not becoming a DevOps engineer. You’ll learn enough infrastructure to deploy and run models, but this isn’t a full DevOps course.
No. The program focuses on deploying, fine-tuning, and building with existing models. Training large models from scratch requires massive compute and is rarely done in most companies. You’ll learn fine-tuning using efficient methods like QLoRA, which is actually practical in real environments. That’s what matters for most AI engineering roles.
Yes. Classes run 3 days per week for 1.5-2 hours (morning or evening slots). You’ll need a few extra hours weekly for labs and projects. The schedule is designed for working professionals.
Strong performers may have opportunities, but placement isn’t guaranteed. The program’s focus is to give you practical, job-ready skills in AI engineering that you can take anywhere.
Enroll Now

Featured Testimonial

When I started here as a trainee, I was finding my footing, but with the right guidance and real project experience, I’ve grown into my role as an Associate AI Engineer. Working on meaningful problems alongside experienced teammates made all the difference, and the growth, though not instant, was real, and I truly value it.

Barnnita Shrestha

Trainee -> Now Associate AI Engineer

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