Course Content
Module 1 : What Is AI Engineering?
After this lesson, learners will be able to: Identify where AI adds real business value Apply Jobs-To-Be-Done (JTBD) for problem discovery Score AI ideas using Pain × Frequency × Cost × Urgency Prioritize high-ROI AI opportunities
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Overview of AI vs ML vs DL
11:07
Role of AI engineers
13:48
Jobs-To-Be-Done (JTBD) Framework for AI
04:30
Quiz-1
00:10:00
Assignment -1
Module 2 :Data Foundations
Data is the backbone of any AI or Machine Learning system. High-quality, well-structured, and relevant data dramatically improves model performance, accuracy, and reliability. Understanding how data is collected, processed, and prepared is essential for building robust AI solutions.
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Types of Data & Labeling
02:39
Feature Engineering Basics
05:12
Data Pipelines
05:25
Quiz-2
Assignment-2
Module 3: ML Fundamentals for Engineers
Machine Learning (ML) enables systems to automatically learn patterns from data and make predictions or decisions. Engineers must understand the core types of ML problems, how to evaluate models, and how to manage errors and generalization to build reliable AI systems.
0/6
Core ML Problem Types
05:40
Production Data Pipelines: Airflow, Kafka, dbt
Data Contracts, Quality, and Governance
Compliance: GDPR, HIPAA, SOC 2 Standards
Quiz-3
Assignment-3
AI Engineering

AI Engineers play a critical role in designing, building, and deploying intelligent systems that can learn, reason, and make decisions. They combine expertise in software engineering, machine learning, data science, and problem-solving to create AI-driven solutions that improve efficiency, automate tasks, and enable new capabilities across industries.

1. Designing and Developing AI Models

AI Engineers build machine learning and deep learning models to solve specific business or technical problems. This includes:

  • Selecting the right algorithms and architectures

  • Training and tuning models

  • Ensuring model accuracy, reliability, and scalability

They work with frameworks such as TensorFlow, PyTorch, Scikit-learn, and others.

2. Data Preparation and Management

High-quality data is essential for effective AI. AI Engineers:

  • Collect, clean, and preprocess data

  • Build pipelines for continuous data ingestion

  • Work with databases and big-data technologies (e.g., SQL, NoSQL, Spark)

3. Integrating AI Into Applications

AI Engineers transform models into real-world applications by:

  • Building APIs and microservices for model deployment

  • Integrating AI capabilities into existing systems

  • Optimizing models for performance in production environments

They often collaborate with software developers and DevOps teams.

4. Monitoring and Improving AI Systems

AI models require ongoing evaluation. AI Engineers:

  • Monitor model performance after deployment

  • Detect data drift or model degradation

  • Retrain or update models to maintain accuracy

They ensure the AI continues to operate ethically and effectively over time.

5. Ensuring Responsible and Ethical AI Use

AI Engineers play a role in:

  • Implementing fairness, transparency, and accountability

  • Reducing bias in datasets and models

  • Ensuring compliance with regulations and organizational policies

6. Collaboration and Problem-Solving

AI Engineers work closely with:

  • Data scientists to refine models

  • Product teams to understand business needs

  • Stakeholders to translate AI results into actionable insights

They act as the bridge between AI technologies and business outcomes.

Conclusion

AI Engineers are essential in bringing artificial intelligence to life. They combine advanced technical skills with strategic thinking to build intelligent solutions that drive innovation, improve decision-making, and create value across industries.

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