Generative AI Course in Bangalore
Built for engineers, developers, AI practitioners, and technical professionals in Bangalore who want more than surface-level GenAI demos, this program focuses on how modern LLM systems are designed, evaluated, integrated, and deployed. You will work across LLMs, RAG, LangChain, VLMs, agent workflows, MCP-style tool integration, evaluation, tracing, and production-minded implementation.
Designed for Serious Working Professionals
Classes are live, mentor-led, and structured. The point is not just access to GenAI content, but technical teaching, implementation clarity, and real discussion.
The format works for professionals managing demanding roles across Bangalore's product teams, startups, GCCs, and engineering organizations without depending on offline attendance.
The program covers LLMs, RAG, multimodal systems, agents, evaluation, tracing, and deployment as one connected workflow instead of isolated buzzwords.
You build portfolio-ready work that is easier to explain in technical interviews, product conversations, and architecture reviews.
What You Will Learn in Practice
LLM and Prompt Workflow Design
Work through prompting patterns, structured outputs, chaining logic, tool use, and workflow design so LLM applications behave more predictably.
Retrieval, Grounding, and Evaluation
Build RAG systems with chunking, hybrid retrieval, reranking, citations, and evaluation loops that make quality easier to inspect and improve.
Multimodal and Agent Workflows
Go beyond text-only use cases with VLMs, multimodal pipelines, agent patterns, tool orchestration, and MCP-style interface thinking.
Tracing and Deployment Thinking
Inspect traces, review failure paths, think about latency and cost, and move systems toward more credible local or cloud deployment patterns.
Explore the full curriculum
This Bangalore page gives you the city-specific view. To explore the complete Generative AI curriculum, tools, projects, and certification details, use the main course page.
No signup required — explore at your own pace
What you'll find on the main page
Complete Module View
See the full curriculum structure beyond the Bangalore-specific narrative.
Project Scope
Review the broader project mix, case studies, and capstone depth in one place.
Certification Details
Understand how the certificate fits into the overall program structure.
Flagship Program View
Use the main page when you want the full non-city version of the course.
Why This Context Matters
Bangalore remains one of India's strongest contexts for learning Generative AI because product teams, GCCs, research groups, and applied AI startups all push beyond basic prompting into systems that need to work in production.
For professionals working across product engineering, internal AI tools, enterprise platforms, copilots, search-backed assistants, and multimodal workflows, the useful shift is no longer just AI awareness. It is the ability to build grounded, reviewable, and deployable GenAI systems. That is why RAG quality, evaluation, tracing, and runtime thinking matter as much as model familiarity.
What Sets This Program Apart
The difference is not just the list of tools. It is the focus on system quality, reviewability, and delivery.
| Features | School of Core AI | Other Institutes |
|---|---|---|
| Learning Format | ✓Live mentor-led sessions with recordings, structure, and technical guidance. | ✗Often a mix of videos, limited interaction, or unclear live support. |
| Curriculum Depth | ✓Covers LLMs, RAG, LangChain, agents, multimodal workflows, evaluation, tracing, and deployment thinking as one connected workflow. | ✗Often focuses only on prompting, Python basics, or a lighter overview of AI tools. |
| Projects & Case Studies | ✓Projects focus on prompt workflows, RAG, multimodal pipelines, agents, and explainable architecture choices. | ✗Projects often stay closer to mini demos and are harder to defend in serious interviews. |
| Trainers | ✓Mentor-led guidance focused on real implementation choices, failure modes, and technical tradeoffs. | ✗Teaching often stays at the concept or tool-demo level. |
| Mentorship | ✓Review support, mentor feedback, and help presenting project work more credibly. | ✗Support is often limited or not built around real technical review. |
| Working Professional Fit | ✓Designed for people who need strong teaching and technical access without offline attendance. | ✗Schedule and support quality are often not designed around serious full-time professionals. |
| Interview and Portfolio Value | ✓You leave with systems and tradeoffs you can explain clearly in interviews and project discussions. | ✗Learners often finish with examples that do not travel well into technical evaluation. |
Live mentor-led sessions with recordings, structure, and technical guidance.
Often a mix of videos, limited interaction, or unclear live support.
Covers LLMs, RAG, LangChain, agents, multimodal workflows, evaluation, tracing, and deployment thinking as one connected workflow.
Often focuses only on prompting, Python basics, or a lighter overview of AI tools.
Projects focus on prompt workflows, RAG, multimodal pipelines, agents, and explainable architecture choices.
Projects often stay closer to mini demos and are harder to defend in serious interviews.
Mentor-led guidance focused on real implementation choices, failure modes, and technical tradeoffs.
Teaching often stays at the concept or tool-demo level.
Review support, mentor feedback, and help presenting project work more credibly.
Support is often limited or not built around real technical review.
Designed for people who need strong teaching and technical access without offline attendance.
Schedule and support quality are often not designed around serious full-time professionals.
You leave with systems and tradeoffs you can explain clearly in interviews and project discussions.
Learners often finish with examples that do not travel well into technical evaluation.
How Learning Happens Here
Live Mentor-Led Sessions
Live classes focus on building real GenAI systems, not just slides. Concepts are taught alongside practical workflow design and system reasoning.
Recordings and Flexible Learning
Sessions are recorded so working professionals can revisit architecture explanations, debugging walkthroughs, and implementation details when needed.
Hands-On Projects
Progress through project work covering LLM applications, RAG systems, multimodal pipelines, agents, evaluation, and deployment-oriented thinking.
Mentor Reviews and Technical Guidance
Questions around architecture, tool choice, RAG quality, evaluation, and debugging get addressed through mentor reviews rather than being left to self-study.
Mock Interviews and Portfolio Prep
Practice technical discussions around GenAI architecture, RAG design, evaluation, and system tradeoffs so your portfolio work is easier to defend.
Career Guidance and Review Support
Get help with resume framing, project storytelling, interview preparation, and role mapping for GenAI-relevant positions.
What You Will Build
You will leave with project work that looks like systems building, not just AI prompting.
Expect work across prompt workflows, grounded RAG, multimodal pipelines, evaluation harnesses, trace-aware debugging, and deployment-minded GenAI applications. The goal is to leave with projects you can explain in interviews, engineering discussions, and product reviews.
What You Will Learn and Be Able to Do
Build Serious LLM Applications
Move from simple prompts into workflow design, structured outputs, chaining logic, tool use, and application patterns that are easier to maintain.
Work Across the Modern GenAI Stack
Build depth across LLMs, VLMs, retrieval systems, vector databases, multimodal workflows, agents, evaluation, safety, and deployment-oriented implementation.
Build Projects with Production Awareness
Implement end-to-end builds that include prompt and retrieval design, traces, evaluation, latency and cost thinking, and deployment patterns that go beyond notebook demos.
Explain Your Work More Credibly
Leave with a capstone, portfolio-ready projects, and clearer reasoning around model choice, RAG design, agent behavior, evaluation results, and deployment decisions.
How the 5 Months Unfold
Weeks 1-2: Foundations
- Python refresh, notebooks, and reliable prompting patterns
- Tokens, embeddings, vector search, and structured outputs
Weeks 3-4: LLMs and VLMs
- Model selection, serving choices, and runtime basics
- Fine-tuning concepts, VLM basics, and document or OCR-oriented workflows
Weeks 5-7: RAG Systems
- Chunking strategy, metadata, retrieval design, and hybrid search
- Reranking, grounding, citations, semantic caching, and quality review
Weeks 8-9: Agents and Orchestration
- LangChain, LangGraph, CrewAI, tool use, and retries
- Memory, orchestration choices, and MCP-style workflow patterns
Weeks 10-12: Evaluation and Safety
- DeepEval, RAGAS, review sets, and evaluation logic
- Guardrails, safety policies, and red-teaming mindset
Weeks 13-16: Deployment and LLMOps
- Tracing, observability, latency and cost thinking
- Cloud deployment patterns, CI/CD basics, and production-minded release decisions
Certification
Earn a verifiable certificate after completing the program and project reviews, with demonstrated work across LLM systems, RAG workflows, multimodal pipelines, evaluation, tracing, and deployment-oriented implementation.
Certificate of Completion
Issued by School of Core AI upon successful completion of the programme
The Learning Community Around the Program
Built for Working Professionals
Where Learners and Alumni Work
What Learners Actually Say
Hiring Partners
Career Opportunities for Generative AI in Bangalore
Across Bangalore product teams, GCCs, research-heavy environments, and AI-first startups, the useful hiring signal is shifting toward people who can build systems that are grounded, reviewable, and deployable.
That makes skills like retrieval quality, multimodal design, evaluation, tracing, and rollout thinking more valuable than surface-level GenAI familiarity alone.
Design end-to-end GenAI features with prompting, RAG, orchestration, evaluation, and safety guardrails that can actually survive product use.
Build and deploy ML and LLM services, reason about latency and cost, and support the infrastructure and workflows behind production AI systems.
Frame business problems, experiment with models, and increasingly work with GenAI tools for search, automation, summarization, and decision support workflows.
Explore new architectures, fine-tuning methods, multimodal workflows, and evaluation techniques in research-oriented or frontier product teams.
Support responsible AI efforts through safety reviews, bias checks, guardrails, evaluation policy, and deployment governance.
Skills That Matter on Real Teams
The shift in Bangalore is not just toward using LLMs. It is toward building GenAI systems that can be measured, improved, and integrated into real products and workflows.
Related Paths
If you are comparing broad Generative AI learning with adjacent specialization paths, start here.
Generative AI Course Fee in Bangalore
Single transparent fee covering complete training, real-world projects, certification and placement support. EMI and part-payment options are available with our counsellor.
Final fee, EMI plans and any ongoing offers will be confirmed by your counsellor based on your batch, mode and payment preference.
Common Questions Before You Join
It is delivered in a live online format with recordings. This Bangalore page is the city-specific view of the broader Generative AI program.