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All Levels · AI · GenAI · MLOps

AI Engineer Program

Design, build, deploy and manage AI-powered applications using machine learning, deep learning, generative AI, large language models, vector databases, cloud AI platforms and production-grade MLOps workflows.

Follow a structured 5–6 months roadmap from foundations to applied ai engineer capability. Practise through 15+ projects, guided labs, case studies and mentor-reviewed assignments. Use Python, NumPy, Pandas, TensorFlow in realistic workflows rather than disconnected demonstrations.
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A-56, Sector-64, Noida, Uttar Pradesh – 201301 Transparent program guidance
School of Artificial Intelligence learning pathway
PythonNumPyPandas
Flagship pathway15+ projects and a portfolio-ready capstone

Program at a glance

Duration5–6 months10–12 hours per week
Next batch23 Aug 2026Open for Registration
Learning modeLive Online / ClassroomAI · GenAI · MLOps
LevelAll LevelsFoundations and guided progression
Projects15+ projectsGuided work plus capstone
CertificateCompletion certificatesubject to published attendance, assignment, project and assessment criteria.

Designed for applied outcomes

Why learners choose this program.

01

Follow a structured 5–6 months roadmap from foundations to applied ai engineer capability.

02

Practise through 15+ projects, guided labs, case studies and mentor-reviewed assignments.

03

Use Python, NumPy, Pandas, TensorFlow in realistic workflows rather than disconnected demonstrations.

04

Build a portfolio-ready capstone and explain the decisions, validation and results.

05

Prepare for AI Engineer and Machine Learning Engineer role conversations with structured career guidance.

Flagship program curriculum

Review every module and the skills covered.

Open each module to compare the detailed syllabus with your current skills and target role before enrolment. Curriculum reviewed 08 Jul 2026.
01Module 1Python for AI Engineering
What learners master

Python syntax, functions, OOP, files, exception handling, environments, packages, APIs, JSON, NumPy, Pandas, Matplotlib and automation.

Detailed syllabus

Topics covered

12 focused topics
  1. 01

    Python syntax

  2. 02

    functions

  3. 03

    OOP

  4. 04

    files

  5. 05

    exception handling

  6. 06

    environments

  7. 07

    packages

  8. 08

    APIs

  9. 09

    JSON

  10. 10

    NumPy

  11. 11

    Pandas

  12. 12

    Matplotlib and automation

02Module 2Mathematics for Machine Learning
What learners master

Linear algebra, probability, statistics, distributions, hypothesis testing, optimization, gradients, loss functions and model interpretation.

Detailed syllabus

Topics covered

8 focused topics
  1. 01

    Linear algebra

  2. 02

    probability

  3. 03

    statistics

  4. 04

    distributions

  5. 05

    hypothesis testing

  6. 06

    optimization

  7. 07

    gradients

  8. 08

    loss functions and model interpretation

03Module 3Data Preparation and Feature Engineering
What learners master

Data collection, missing values, outliers, encoding, scaling, leakage prevention, train-test split, feature selection and exploratory analysis.

Detailed syllabus

Topics covered

8 focused topics
  1. 01

    Data collection

  2. 02

    missing values

  3. 03

    outliers

  4. 04

    encoding

  5. 05

    scaling

  6. 06

    leakage prevention

  7. 07

    train-test split

  8. 08

    feature selection and exploratory analysis

04Module 4Machine Learning
What learners master

Regression, classification, clustering, trees, random forests, gradient boosting, XGBoost, tuning, cross-validation, explainability and evaluation metrics.

Detailed syllabus

Topics covered

10 focused topics
  1. 01

    Regression

  2. 02

    classification

  3. 03

    clustering

  4. 04

    trees

  5. 05

    random forests

  6. 06

    gradient boosting

  7. 07

    XGBoost

  8. 08

    tuning

  9. 09

    cross-validation

  10. 10

    explainability and evaluation metrics

05Module 5Deep Learning
What learners master

Neural networks, backpropagation, optimizers, TensorFlow, PyTorch, CNNs, RNNs, LSTMs, transformers, transfer learning and tuning.

Detailed syllabus

Topics covered

10 focused topics
  1. 01

    Neural networks

  2. 02

    backpropagation

  3. 03

    optimizers

  4. 04

    TensorFlow

  5. 05

    PyTorch

  6. 06

    CNNs

  7. 07

    RNNs

  8. 08

    LSTMs

  9. 09

    transformers

  10. 10

    transfer learning and tuning

06Module 6Natural Language Processing
What learners master

Tokenization, embeddings, sentiment analysis, named entity recognition, semantic search, summarization, question answering and transformer NLP.

Detailed syllabus

Topics covered

7 focused topics
  1. 01

    Tokenization

  2. 02

    embeddings

  3. 03

    sentiment analysis

  4. 04

    named entity recognition

  5. 05

    semantic search

  6. 06

    summarization

  7. 07

    question answering and transformer NLP

07Module 7Generative AI and Large Language Models
What learners master

LLM architecture, prompt engineering, structured outputs, function calling, RAG, AI agents, guardrails, evaluation and responsible AI.

Detailed syllabus

Topics covered

8 focused topics
  1. 01

    LLM architecture

  2. 02

    prompt engineering

  3. 03

    structured outputs

  4. 04

    function calling

  5. 05

    RAG

  6. 06

    AI agents

  7. 07

    guardrails

  8. 08

    evaluation and responsible AI

08Module 8Vector Databases and RAG Systems
What learners master

Embeddings, semantic and hybrid search, chunking, metadata filters, retrieval evaluation, LangChain, LlamaIndex and enterprise document search.

Detailed syllabus

Topics covered

7 focused topics
  1. 01

    Embeddings

  2. 02

    semantic and hybrid search

  3. 03

    chunking

  4. 04

    metadata filters

  5. 05

    retrieval evaluation

  6. 06

    LangChain

  7. 07

    LlamaIndex and enterprise document search

09Module 9AI Application Development
What learners master

FastAPI, Flask, Streamlit, Gradio, REST APIs, authentication, databases, model serving, UI integration and chatbot development.

Detailed syllabus

Topics covered

9 focused topics
  1. 01

    FastAPI

  2. 02

    Flask

  3. 03

    Streamlit

  4. 04

    Gradio

  5. 05

    REST APIs

  6. 06

    authentication

  7. 07

    databases

  8. 08

    model serving

  9. 09

    UI integration and chatbot development

10Module 10MLOps and AI Deployment
What learners master

Model packaging, Docker, CI/CD, registries, experiment tracking, MLflow, monitoring, drift detection, logging and cloud deployment.

Detailed syllabus

Topics covered

9 focused topics
  1. 01

    Model packaging

  2. 02

    Docker

  3. 03

    CI/CD

  4. 04

    registries

  5. 05

    experiment tracking

  6. 06

    MLflow

  7. 07

    monitoring

  8. 08

    drift detection

  9. 09

    logging and cloud deployment

11Module 11Responsible AI, Governance and Security
What learners master

Bias, explainability, privacy, AI risk controls, secure prompt design, red teaming, PII handling and AI governance.

Detailed syllabus

Topics covered

7 focused topics
  1. 01

    Bias

  2. 02

    explainability

  3. 03

    privacy

  4. 04

    AI risk controls

  5. 05

    secure prompt design

  6. 06

    red teaming

  7. 07

    PII handling and AI governance

Tools and platforms

Use the practical stack behind the program.

Platform access and software requirements are confirmed before the cohort begins.
PythonNumPyPandasTensorFlowPyTorchLangChainLlamaIndexPineconeFAISSChromaFastAPIDockerMLflow

Portfolio-ready capstone

Build work you can explain, validate and present.

Build an end-to-end AI solution such as an AI resume screening system, enterprise knowledge chatbot, healthcare document assistant, finance AI agent or HR automation assistant.
Final applied project

AI Engineer capstone

Project direction
Build an end-to-end AI solution such as an AI resume screening system, enterprise knowledge chatbot, healthcare document assistant, finance AI agent or HR automation assistant.
Evidence 01
A documented problem statement, scope and success criteria.
Evidence 02
A working implementation with architecture or process documentation.
Evidence 03
Testing, validation and limitations recorded against a review checklist.
Portfolio evidenceA documented problem statement, scope and success criteria.
Portfolio evidenceA working implementation with architecture or process documentation.
Portfolio evidenceTesting, validation and limitations recorded against a review checklist.
Portfolio evidenceA portfolio case study and presentation-ready project walkthrough.

Who should enrol

Confirm that this program matches your goals and starting point.

Students, fresh graduates, developers, data professionals, analysts, cloud engineers, working professionals and career switchers.
  • Live online classes, hands-on labs, mentor-led projects, case studies, assessments, a capstone project, interview preparation and portfolio building.
  • Plan for 10–12 hours per week.

Learning methodology

Live guidance, deliberate practice and reviewable outputs.

01

Live expert instruction

Live online classes, hands-on labs, mentor-led projects, case studies, assessments, a capstone project, interview preparation and portfolio building.

02

Guided hands-on practice

Apply each major concept through labs, case studies and 15+ projects.

03

Milestone feedback

Progress is reviewed through python and data foundation checks, model-building and genai labs, mentor-reviewed milestone projects, capstone demonstration and technical review.

04

Portfolio validation

Document implementation decisions, test results, limitations and business or technical value.

Assessment approachEvidence-based progress, not passive attendance

Python and data foundation checks · Model-building and GenAI labs · Mentor-reviewed milestone projects · Capstone demonstration and technical review

Career direction

Translate program work into a credible professional story.

Build role-aligned skills, project evidence and interview confidence for AI Engineer, Machine Learning Engineer, GenAI Developer, LLM Engineer, Applied AI Developer, AI Product Engineer, AI Automation Specialist, Junior MLOps Engineer opportunities.
  • AI Engineer
  • Machine Learning Engineer
  • GenAI Developer
  • LLM Engineer
  • Applied AI Developer
  • AI Product Engineer
  • AI Automation Specialist
  • Junior MLOps Engineer
1

Portfolio and project review

Improve project structure, documentation, evidence and the clarity of each walkthrough.

2

Resume and profile guidance

Connect verified program capabilities and project outputs to a focused professional profile.

3

Interview preparation

Practise explaining technical decisions, trade-offs, results and limitations with confidence.

4

Application planning

Identify relevant role families and create a practical, consistent application plan.

Career support is not an employment guarantee. Outcomes depend on experience, participation, project quality, interviews, employer requirements and market conditions.

Noida learning centre

Local guidance for learners across Delhi NCR.

FutureEdgeAI Academy supports live online and classroom-guided flagship program cohorts according to each published batch format.
A-56, Sector-64, Noida, Uttar Pradesh – 201301
Get directions Call admissions

Program FAQs

Clear answers before you enrol.

Who should join the AI Engineer Program?

Students, fresh graduates, developers, data professionals, analysts, cloud engineers, working professionals and career switchers.

Do I need prior experience?

No AI experience is required. Basic programming familiarity is helpful; learners without Python experience should complete the provided foundation preparation before the machine-learning modules.

How much time should I plan each week?

Plan approximately 10–12 hours per week, including live sessions, guided labs, assignments, revision and portfolio work.

Are classes live or recorded?

The program includes live instructor-led classes. Recording access, notes, assignments and learning resources are provided according to the published cohort format.

What practical work will I complete?

15+ projects are included across guided labs, applied assignments and portfolio builds. The capstone direction is: Build an end-to-end AI solution such as an AI resume screening system, enterprise knowledge chatbot, healthcare document assistant, finance AI agent or HR automation assistant.

How will my progress be assessed?

Python and data foundation checks; Model-building and GenAI labs; Mentor-reviewed milestone projects; Capstone demonstration and technical review. Learners receive feedback at key milestones before the capstone review.

Which tools will I use?

Learners practise with Python, NumPy, Pandas, TensorFlow, PyTorch, LangChain, LlamaIndex, Pinecone, FAISS, Chroma, FastAPI, Docker, MLflow. Published tools may be updated to reflect current workflows and platform availability.

Is career and placement assistance included?

Career support includes roadmap guidance, resume and profile improvement, portfolio reviews, mock interviews and opportunity visibility. Employment or placement is not guaranteed.

Will I receive a certificate?

A program completion certificate may be issued after the learner meets the published attendance, assignment, project and assessment requirements.

Current fee: ₹30,000. Payment options and refund terms are explained before payment. Review the academy's refund policy for published terms.

Learner proof

Practical learning, explained by the people who experienced it.

Published learner feedback from FutureEdgeAI pathways shows how guided projects, mentor review and career preparation can help learners communicate their work with greater confidence.
Explore career support
5.0
“The SOC project helped me practise alert triage, investigation notes, incident response and the professional reporting expected from analysts.”
Arjun Mehta, Cybersecurity Learner
Arjun MehtaCybersecurity Learner
5.0
“Building one product end to end—React, APIs, authentication, databases, testing and deployment—made my portfolio much more credible.”
Priya Menon, Full Stack Developer
Priya MenonFull Stack Developer
5.0
“I progressed from AI fundamentals to building GenAI applications and a source-aware RAG assistant I could confidently demonstrate.”
Neha Singh, Applied AI Learner
Neha SinghApplied AI Learner

Individual learning and career outcomes vary by starting point, participation, project quality, experience and market conditions. Testimonials do not guarantee employment or placement.

Ready to explore AI Engineer Program?

Ready to explore the AI Engineer Program?

Review the curriculum, confirm prerequisites, see the next batch and speak with admissions before enrolment.
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