If you're a developer exploring LLMs, AI agents, or modern Generative AI workflows, you’ve probably noticed how quickly the ecosystem is evolving. Tools like LangChain, Hugging Face, ChatGPT APIs, vector databases, and Agentic AI frameworks are becoming essential across engineering teams.
For Pune-based developers (or those who prefer hybrid/online formats), TechnoGeeks offers a hands-on Generative AI + Agentic AI course that focuses on practical implementation, code-first learning, and real-world AI workflows.
This write-up gives a developer-friendly overview of what the program includes, why it’s useful, and what skills you can expect to build.
Why Developers Are Transitioning to Generative AI and Agentic AI
Modern software development is shifting toward:
LLM-powered applications
Retrieval Augmented Generation (RAG)
Multi-agent workflows
Autonomous task execution
Fine-tuning and deployment of large language models
AI-enhanced data engineering
Automation using Python, APIs, and vector databases
Generative AI isn’t just a niche domain anymore — it directly intersects with backend engineering, API integration, DevOps, cloud development, and data science.
This makes Generative AI training a strong upskilling path for developers, analysts, and ML beginners.
What the TechnoGeeks Generative AI + Agentic AI Course Covers (Developer Focus)
Core Gen AI Concepts
Transformer architecture
Foundation models
Prompt engineering
Model evaluation
RAG systems
Vector search
Embedding models
LLM Engineering
Working with ChatGPT APIs
LangChain workflows
LLM fine-tuning
Model serving
Deployment with Python
Integrating LLMs into applications
Agentic AI Modules
Agent architecture
Tool calling
Memory & context management
Multi-agent pipelines
Practical agent design for production use
Practical Tools
Python for AI
LangChain
Hugging Face
DALL·E
NLP libraries
Open-source LLMs
Cloud-based workflows
Developer Projects
LLM-powered chat interfaces
Agent-based task automation
RAG-enabled knowledge assistants
Custom fine-tuned models
Text-to-image and code generation tools
AI assistants for data engineering tasks
The course includes an optional placement pathway for those seeking structured career support.
Who This Course Helps
This program fits well for:
Developers getting into AI
Backend engineers exploring LLM integrations
Data analysts transitioning to AI/ML
ML beginners seeking practical exposure
Working professionals wanting a structured roadmap
Students preparing for job-oriented Gen AI roles
If you’re already comfortable with Python and APIs, you'll find the hands-on sections especially useful.
Pune Locations and Training Format
TechnoGeeks provides training in multiple Pune tech areas including:
Hinjewadi
Baner
Kharadi
Aundh
Hadapsar
Magarpatta
Wakad
Shivajinagar
Remote and online formats are available as well, which helps developers join from anywhere.
Career Outlook (Developer Perspective)
Developers transitioning into Generative AI and Agentic AI are moving into roles such as:
LLM Engineer
Generative AI Engineer
Agentic AI Developer
AI Automation Engineer
NLP Engineer
Data Science with Gen AI roles
Machine Learning Engineer (Gen AI)
These roles focus heavily on hands-on implementation rather than pure research, making them highly accessible for developers with strong fundamentals.
Final Thoughts
If you're a developer aiming to understand how LLMs, agentic AI, RAG, and fine-tuning work in real-world engineering workflows, the TechnoGeeks Generative AI and Agentic AI course in Pune offers a structured, project-driven path to get you started.
It’s practical, coding-oriented, and built for developers who want to apply AI to real systems — not just learn concepts in isolation.
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