AI Engineer Bootcamp

WhatsApp +91 93517 82674 · Mon-Sat, 9 AM - 8 PM

Assure Success

About the course

Why take AI Engineer Bootcamp?

The AI Engineer Bootcamp is aimed at people who want to build and run AI-powered software, not only train models in notebooks. It treats AI engineering as software engineering plus applied machine learning: clean code, testing, APIs, data pipelines, evaluation and monitoring all matter as much as model choice.

The programme starts with Python engineering practice and the machine learning and deep learning concepts you need to reason about models. It then moves to the modern application layer: working with LLM APIs and open models, structured outputs, embeddings and vector search, retrieval-augmented generation, tool calling and agent workflows. A large part of the course is about what goes wrong in production, such as hallucinations, prompt injection, latency, cost and drift, and how to evaluate and monitor systems systematically.

There is no external exam. You complete several projects and a capstone, and receive an Assure Success course-completion certificate. We do not promise jobs or placements.

Who should attend

● Software developers who want to build AI features and products
● Data scientists who want to move from notebooks to deployed systems
● Backend and cloud engineers moving into AI engineering roles
● Graduates with programming skills targeting applied AI careers
● Technical team leads responsible for delivering AI projects

Prerequisites

● Working programming skills, preferably in Python
● Basic understanding of APIs and databases
● Basic mathematics and statistics; machine learning basics are covered in the course

What this certification does for you

Engineering focus

Goes beyond demos to testing, deployment and operations.

Modern AI stack

Includes RAG, agents, evaluation and guardrails, which most AI job descriptions now mention.

Several projects

Multiple applications built, reviewed and documented for your portfolio.

Safety and cost awareness

You learn to reason about risk and budget, not only accuracy.

Structured, mentor-led pace

Scheduled live sessions keep a long programme on track.

Curriculum

10 modules, step by step

MODULE 1

Python Engineering, Data and APIs

Write maintainable code and work with data services.

› Advanced Python and typing
› Testing with pytest
› Git workflows and code review
› SQL and NoSQL basics
› REST APIs with FastAPI
› Authentication and rate limits

MODULE 2

Machine Learning Foundations

Understand core ML.

› Regression, classification, clustering
› Feature engineering and validation
› Gradient boosting
› Metrics and error analysis

MODULE 3

Deep Learning

Train and adapt neural networks.

› Neural network training
› CNNs and transfer learning
› Sequence models
› Attention and transformers

MODULE 4

Working with Language Models

Use LLMs effectively.

› Model families and API usage
› Prompt design and structured outputs
› Function / tool calling
› Open models and local inference options

MODULE 5

Embeddings, Vector Search and RAG

Ground models in your data.

› Embeddings and similarity
› Vector databases and hybrid search
› Chunking and re-ranking
› RAG evaluation

MODULE 6

Fine-Tuning and Adaptation

Know when and how to adapt models.

› Prompting vs RAG vs fine-tuning
› LoRA and parameter-efficient methods
› Dataset preparation
› Evaluation after fine-tuning

MODULE 7

Agents and Workflows

Build multi-step AI systems.

› Agent loops and planning
› Tool design and error handling
› State, memory and orchestration frameworks
› Human-in-the-loop controls

MODULE 8

Evaluation, Safety and Guardrails

Make systems trustworthy.

› Test sets and automated evaluation
› Hallucination and bias checks
› Prompt injection and data leakage
› Content and policy filters

MODULE 9

Deployment, MLOps and System Design

Run AI in production and design for scale.

› Docker and CI/CD
› Cloud deployment options
› Experiment tracking and model registry
› Latency and caching
› Queueing and batching
› Privacy and compliance considerations

MODULE 10

Capstone and Career Preparation

Deliver and present an AI application.

› Capstone: build, evaluate and deploy
› Architecture review
› Portfolio and GitHub guidance
› Mock technical interviews

What you get

Why take AI Engineer Bootcamp?

Who should attend

Prerequisites

What this certification does for you

10 modules, step by step

Certified by Assure Success

Your path to certification

Skills you will gain

Where this course can take you

Why learn with Assure Success