Senior AI Engineer Program

Advance your AI career with our expert-level Senior AI Engineer program. Gain deep expertise in NLP, Big Data, MLOps, and AI Research through 780 hours of intensive, hands-on learning.

840 Hrs

Course Duration

Online

Course Type

1.08.2025

Course Start

Master the skills to lead complex AI projects and
shape the future of intelligent systems.

This rigorous program is crafted for experienced professionals seeking to elevate their AI capabilities. Dive deep into advanced topics like NLP with Transformers, scalable Big Data–AI integration, full-cycle MLOps, and AI Research & Development. Ideal for those aiming to architect, deploy, and lead enterprise-grade AI systems.

learning

Expert-Level
Curriculum

Four modules, 780 total hours, designed for experienced AI practitioners.

doc

Industry-Centric
Training

Work on real-world use cases and advanced capstone projects.

industry

Career-Boosting
Certification

Showcase your expertise with industry-recognized credentials.

Senior AI Engineer Program

Program Overview

The Senior AI Engineer program equips professionals with advanced technical and strategic skills to design, build, and scale intelligent systems across diverse domains. This program combines theory with rich project-based learning, preparing you for leadership roles in AI and data science.

Key Points

  • icon Designed for professionals with prior experience in AI or data science.
  • icon Extensive real-world projects and capstone challenges.
  • icon Learn from top-tier instructors and domain experts.

Course Features

Total Hours:

840

Total Weeks:

21

Skill Level:

Intermediate

Note

Become a certified AI Team Lead with applied skills in AI strategy and execution.

Curriculum

  • Natural Language Processing (NLP) Specialization – 180 hours / 12 weeks
  • Big Data and AI Integration – 120 hours / 8 weeks
  • Machine Learning Operations (MLOps) – 180 hours / 12 weeks
  • Mastering Data Science & AI Ecosystem – 180 hours / 12 weeks
  • AI Research and Development Skills – 180 hours / 12 weeks

Course Features

Total Hours:

840

Total Weeks:

21

Skill Level:

Intermediate

Note

Become a certified AI Team Lead with applied skills in AI strategy and execution.

Industry Certification

  • AIE by Artificial Intelligence Board of America (ARTiBA)
  • Data Science Council of America (DASCA) – Big Data Engineering Certifications
  • Google Professional Machine Learning Engineer
  • AWS Certified Machine Learning – Specialty
  • Microsoft Certified: AI-900 AI Fundamentals & Azure AI Engineer Associate
  • Certified Artificial Intelligence Scientist (CAIS™)

Earn a prestigious certification that validates your ability to architect, manage, and deploy AI systems at scale—an essential credential for advanced AI roles in leading organizations.

Course Features

Total Hours:

840

Total Weeks:

21

Skill Level:

Intermediate

Note

Become a certified AI Team Lead with applied skills in AI strategy and execution.

Your Questions, Answered

Find quick answers to the most common questions about our courses, instructors, and policies.

Does this course help bridge the gap between research and deployment? icon
Yes, it covers both experimentation and production, helping you move research concepts into usable systems.
Is the course suitable for those interested in AI architecture design? icon
Absolutely. It includes topics on pipeline planning, component integration, and long-term scalability.
Do I need a strong programming background to join? icon
A solid understanding of programming and prior exposure to AI or data science concepts will be helpful.
Can this course help me contribute to open-source AI projects? icon
Yes, you’ll gain experience with tools and practices that align with common open-source workflows.
Will the course content stay relevant over time? icon
It’s designed around foundational principles and current industry trends, so it remains applicable even as tools evolve.
Will I get exposure to industry practices? icon
Yes, the course reflects how AI is applied in professional settings, including workflows, tools, and decision-making approaches.

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