Certification Course · PG Certificate in Applied Generative Engineering & LLM Applications
PG Certificate in Applied Generative Engineering & LLM Applications
6 months | Mode - Online
- Fee
- ₹90,000 + GST
- Schedule
- 6 Months | Sep 30, 2026 - Mar 27, 2027 | online
- Seats
- Few Seats Left
- Enrolled
- 32
- Trainer
- upGrad
- Rating
- ★ 4.5
Overview
About this course
Generative AI is moving rapidly from experimentation to production, creating demand for professionals who can build reliable, scalable and secure AI systems. This program goes beyond prompts and prototypes to teach the complete engineering journey—from LLMs and RAG to agents, deployment, evaluation, security and LLMOps.
With 24 weeks of live, hands-on learning, you’ll work with enterprise technologies, build production-oriented AI applications and finish with a deployable capstone. The program is designed to help you develop practical engineering capabilities aligned with the rapidly evolving demands of Applied AI, LLM and Agentic AI roles.
Outcomes
What you'll achieve
- ML Engineers & Generative AI Engineers
- Data Engineers & Data Scientists
- Technical Product Engineers & Solution Architects
- Software Engineers & Backend Developers
- Technical Product & AI Leaders
- Implementation Consultant / Professionals
- Production-Focused Curriculum - Covering the complete journey from LLM foundations to building and deploying production-grade AI systems.
- AWS-Integrated Learning - with a guided AWS path across Bedrock, OpenSearch, Lambda, API Gateway and SageMaker.
- Evaluation & Reliability - with a structured approach to measuring LLM, RAG and AI agent performance.
- 24 Weeks of Live Learning - with 140+ hours of live online sessions led by industry practitioners.
- Production-Grade Capstone - to build and deploy an end-to-end AI system as a portfolio-ready artefact.
- Dual Completion Credentials - from iHUB DivyaSampark, IIT Roorkee and upGrad x AWS.
Curriculum
Download the course curriculum
Voices
Learner testimonials
Refresh the foundational Python, notebook and data skills required for the program, while getting hands-on with enterprise-grade AI development tools and environments.
Establish the programming, systems and conceptual foundation required to build, test and integrate LLM-powered applications.
Design reliable prompts and structured LLM interactions, and evaluate outputs for correctness, relevance, consistency and safety.
Build robust knowledge-preparation pipelines that make enterprise and domain data searchable and useful for LLM applications.
Design, implement and evaluate end-to-end RAG systems that generate grounded, traceable and context-aware responses.
Build production-oriented LLM applications with scalable API integrations, efficient request handling and reliable user interactions.
Create tool-using AI agents that can reason over tasks, execute multistep workflows and operate within defined guardrails.
Architect multi-agent systems for complex workflows while balancing autonomy, orchestration, reliability and human oversight.
Deploy and operate LLM and agentic systems with the observability, security, governance and reliability needed for real-world use.
Assess the ethical, governance and business implications of LLM deployments and adapt solutions responsibly to organisational contexts.
Apply the complete engineering stack to design and build a deployable LLM, RAG or agentic AI solution addressing a real business problem.