Accelerate your career in Europe’s AI capital with our Advanced Generative AI and Agentic AI Training in Paris. This program is engineered for the French tech ecosystem, focusing on the industrialization of LLMs, sovereign RAG architectures, and multi-agent orchestration for enterprise-scale deployment. From advanced prompt engineering to building autonomous agents with frameworks like LangChain, you will bridge the gap between R&D and production. Designed for GenAI engineers and automation architects in sectors from FinTech to Luxury, this training emphasizes performance optimization, GDPR-compliant workflows, and the strategic integration of agentic systems into the global digital value chain.
High Demanding
Advanced Generative Ai and Agentic Ai Training in Paris
Master Generative AI and Agentic AI workflows in Paris. Build autonomous systems and drive digital impact with Techpratham’s elite industry-led training.
5/5(4,890 Reviews)
Level
Advanced
Duration
8 Weeks
Placement Client
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About Advanced Generative Ai and Agentic Ai Training in Paris
Training Plan
01
About trainer
Working professional who is carrying more then 10 years of industry experience.
02
Decks & Updated Content
Access to updated presentation decks shared during live training sessions.
03
e-Book
E-book provided by TechPratham. All rights reserved.
04
Assignments & MCQs
Module-wise assignments and MCQs provided for practice.
05
Video Recording
Daily Session would be recorded and shared to the candidate.
06
Projects
Live projects will be provided for hands-on practice.
07
Resume Building
Expert-guided resume building with industry-focused content support.
08
Interview Preparation
Comprehensive interview preparation with real-time scenario practice.
Advance Generative Ai and Agentic Ai
Python for AI & Automation (Foundation Layer)
Python for AI & Automation (Foundation Layer) builds core Python skills to automate tasks and create a strong base for AI and machine learning development.
Python Essentials for AI
Python syntax, data types, control flow
Functions, modules, virtual environments
OOP concepts (important for agents)
Python for Automation & AI
File handling, APIs, JSON
Web requests, REST API consumption
Async programming basics
Logging, exception handling
AI-Ready Python Libraries
NumPy, Pandas
Requests, FastAPI (intro)
LangChain utilities (preview)
Introduction to Agentic AI & LLM Ecosystem
A foundational overview of how autonomous AI agents work with large language models to plan, reason, and execute tasks across modern AI systems.
What is Agentic AI?
Role of LangGraph, AutoGen, CrewAI in the ecosystem
OpenAI vs Azure OpenAI vs AWS Bedrock
Introduction to foundational concepts: Agents, Tasks, Graphs
Basics of LangChain and LangGraph
An introductory overview of using LangChain and LangGraph to build, connect, and orchestrate intelligent, multi-step AI workflows.
LangChain recap: Chains, Tools, Memory
LangGraph architecture and why it matters
Installation, environment setup, and first LangGraph DAG
Exploring LangGraph Core Concepts
A concise introduction to building stateful, multi-step AI workflows using LangGraph for agent orchestration and decision-making.
Nodes, Edges, State Machines
Understanding transitions and handlers
Building a simple agentic task flow
Python SDK and Node Configuration
An overview of setting up the Python SDK and configuring nodes to build, connect, and manage scalable AI workflows efficiently.
Deep dive into LangGraph Python SDK
Defining nodes and reactive transitions
Testing individual components with unit test strategy
Multi-Agent Setup with LangGraph
A practical introduction to designing and orchestrating multiple AI agents that collaborate and share state using LangGraph.
Multi-agent interaction via graph state
Introducing dynamic task allocation
Conditional logic and loops in graphs
High Demanding Courses
No related courses found
Additional Program Highlights
Comprehensive study materials and resources
HD
Professional resume building session
HD
Master your interview skills
HD
Real-world project demonstrations
HD
Upcoming Batches
Can't find a batch you were looking for?
Who Should Enroll in this Advance Generative Ai and Agentic Ai Training
IT Professionals
Non-IT Career Switchers
Fresh Graduates
Working Professionals
Ops/Administrators/HR
Developers
BA/QA Engineers
Cloud / Infra
IT Professionals
Non-IT Career Switchers
Fresh Graduates
Job Roles After Completing Advance Generative Ai and Agentic Ai Training
Agentic AI Engineer
Generative AI Solutions Architect
RAG (Retrieval-Augmented Generation) Specialist
Cognitive Architecture Designer
AI Workflow Architect
AI Agent Onboarding Manager
Multi-Agent Systems (MAS) Architect
LLM Operations (LLMOps) Engineer
AI Agent Orchestration Specialist
Synthetic Data Engineer
Responsible AI & Governance Lead
AI Product Manager (Agentic Systems)
Agentic AI Engineer
Generative AI Solutions Architect
RAG (Retrieval-Augmented Generation) Specialist
Key Projects
Advance Generative Ai and Agentic Ai
Accenture – Multi-Agent Autonomous Supply Chain Orchestration for Stellantis
Scenario: Designing an agentic digital twin network for automotive manufacturing plants near Paris to predict inventory gaps and trigger automated supply chain re-routing.
Live Work:
- Coded multi-agent graphs using LangGraph.
- Linked real-time IoT logs to ChromaDB.
- Set up LLMOps performance tracking.
Outcome: Reduced supply chain latency by 35% across European assembly lines.
Deloitte – AgenticAdopt Compliance Guard for Paris Financial Institutions
Scenario: Deploying autonomous AI agents to audit complex cross-border financial transactions against strict European Union and sovereign AI regulatory standards.
Live Work:
- Built advanced RAG for legal text data.
- Coded custom Model Context Protocols.
- Integrated automated bias evaluation layers.
Outcome: Achieved 99.4% precision in flagging regulatory compliance risks.
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KPMG – GenAI Factory for Luxury Retail Group Hubs in Paris
Scenario: Implementation of a secure SaaS-based generative AI document intelligence platform to process multi-language supplier invoices and vendor contracts autonomously.
Live Work:
- Maintained production DSPy prompt systems.
- Connected Qdrant vector search engines.
- Automated unstructured data extractions.
Outcome: Decreased operational contract review times by nearly 60%.
Infosys – CrewAI Customer Lifecycle Agents for Telecom Paris
Scenario: Developing a collaborative multi-agent ecosystem that analyzes telecom user behaviors, handles service churn risks, and pushes personalized retention deals.
Live Work:
- Configured role-specific CrewAI frameworks.
- Programmed dynamic fallback agent rules.
- Linked enterprise CRM tools to LLM APIs.
Outcome: Elevated target customer retention rates by 22% in the Île-de-France area.
Placement Process
Live Class with Industry Use Cases
- Interactive learning
- Real-world examples
- Expert guidance
- Doubt resolution
Daily Assignment & Weekly Assessments
- Hands-on practice
- Concept reinforcement
- Weekly evaluation
- Timely feedback
Project Testing & Deployment
- Project development
- Testing practice
- Deployment exposure
- Portfolio creation
Interview Preparation (Mock, PD, Alumni Session)
- Mock interviews
- Personal development
- Alumni guidance
- Confidence building
Resume Building
- Resume crafting
- Industry-focused format
- Skills highlighting
- Professional presentation
Placement
- Job assistance
- Interview support
- Career guidance
- Opportunity access
Latest Hiring
No hiring posts
Recently Placed Candidates
No placements available
Latest Hiring
No hiring posts
Our Success Mantra
Commitment
- Ensuring quality training every day
Fulfillment
- Meeting learning goals with confidence
Accomplishment
- Students achieving industry-ready expertise
Our Learner Voice
Beyond Courses:
Additional Support We Provide
24/7 Support
LinkedIn Profile
Resume Writing
Alumni Sessions
Interview Preparation
Live Projects
What Makes TechPratham Training Different?
Feature
TechPratham Advantage
Industry Standard Institutes
Real-World Implementation
Live enterprise tenant access for 24/7 practice on real-world, enterprise-grade scenarios.
Restricted or simulation-based access that lacks real-world complexity.
Subject Matter Experts
Mentorship from certified professionals with 15+ years of global industry experience.
Generic instructors with no hands-on implementation background.
2026 Ready Curriculum
AI-driven curriculum with advanced platform integrations and real-world configurations.
Basic, outdated syllabus that misses key technical integrations.
Daily & Weekly assignments
Daily assignments and weekly assessments focused on concept reinforcement and continuous feedback.
Self-paced learning with no accountability or progress tracking.
End-to-End Lifecycle
Hands-on project testing and deployment with portfolio-ready real-world exposure.
Basic lab exercises without any deployment or testing practice.
Interview Readiness
Structured interview preparation including mock interviews, PD sessions, and alumni guidance.
Minimal support limited to a generic certificate of completion.
Placement Support
Guaranteed interview support through tie-ups with top MNCs and 1-on-1 mock interview sessions.
Basic career tips with no direct industry connections.
Training Support
24/7 technical doubt resolution and personalized mentoring support.
Limited mentor availability and no support after class hours.
Affordable Fees
Competitive fixed pricing with flexible payment options and transparent fee structure.
Inflated fees with hidden costs and low return on investment.
Real-World Implementation
Live enterprise tenant access for 24/7 practice on real-world, enterprise-grade scenarios.
Restricted or simulation-based access that lacks real-world complexity.
Subject Matter Experts
Mentorship from certified professionals with 15+ years of global industry experience.
Generic instructors with no hands-on implementation background.
2026 Ready Curriculum
AI-driven curriculum with advanced platform integrations and real-world configurations.
Basic, outdated syllabus that misses key technical integrations.
Daily & Weekly assignments
Daily assignments and weekly assessments focused on concept reinforcement and continuous feedback.
Self-paced learning with no accountability or progress tracking.
End-to-End Lifecycle
Hands-on project testing and deployment with portfolio-ready real-world exposure.
Basic lab exercises without any deployment or testing practice.
Interview Readiness
Structured interview preparation including mock interviews, PD sessions, and alumni guidance.
Minimal support limited to a generic certificate of completion.
Placement Support
Guaranteed interview support through tie-ups with top MNCs and 1-on-1 mock interview sessions.
Basic career tips with no direct industry connections.
Training Support
24/7 technical doubt resolution and personalized mentoring support.
Limited mentor availability and no support after class hours.
Affordable Fees
Competitive fixed pricing with flexible payment options and transparent fee structure.
Inflated fees with hidden costs and low return on investment.
How is Agentic AI different from basic Generative AI?
Could you clarify if the curriculum includes hands-on engagement with live industry use cases or real-world projects?"
How does the program integrate career services and placement support to help participants transition into high-growth AI roles?
Can I switch between batches if my schedule changes?
Is there a Certification provided at the end?
Could you please provide the detailed duration , schedule and available slots for the upcoming training sessions?
How do you technically distinguish a "Chain" from an "Agent" when designing an autonomous workflow for complex financial auditing?
Explain the "Swiss Cheese" security model in the context of allowing an AI Agent to execute Python code in a production environment.
If an LLM-based agent constantly hallucinates tool parameters, how would you architect a "Self-Correction" loop without doubling latency?
How do you manage "State Drift" in a multi-agent system where one agent's output invalidates the context held by another agent?
Why would you choose a "Graph-based" orchestration over a "Sequential" one for a multi-step Agentic AI product?
In a RAG system, how do you handle the "Lost in the Middle" phenomenon when your Agent retrieves 50+ documents for a query?
About Advance Generative Ai and Agentic Ai Certification
Industry-Recognized Certification
Advanced Generative Ai and Agentic Ai
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