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GenAI Engineering Training in Bangalore — Build, Deploy & Ship AI Agents
An 8-week, live instructor-led program taking you from neural network fundamentals to a deployed, multi-agent GenAI assistant. Six mini-projects and one capstone — all shipped, not just studied.
Faculty
Curriculum
Format
Outcome
Duration
Batch
US Market Salary Range*
*Market-wide AI/ML engineer salary range, Robert Half 2026 Salary Guide — an industry benchmark, not a claim about this programme…s graduate outcomes.
Why Now — The Market Is Rewarding GenAI and Agent Skills Specifically
The 2026 hiring data, sourced — including the part where agentic-AI demand is growing faster than AI/ML overall.
YoY growth in agentic-AI job postings specifically — the exact skill Module 03 targets. SRC: Final Round AI, 2026
AI Engineer ranked the fastest-growing US job title for the 2nd year running, postings up 143% YoY in 2026. SRC: LinkedIn Jobs on the Rise, Jan 2026
Open AI/ML roles globally — postings up 163% (2026→25), a further 74% YoY in 2026. SRC: Dice / LinkedIn / Axial Search, 2026
Wage premium for roles explicitly requiring AI skills vs. comparable non-AI roles. SRC: PwC 2026, via Final Round AI
Growth in AI engineer interview activity Sept 2026–June 2026 — recruiters say the bottleneck is qualified candidates. SRC: Final Round AI, 2026
Share of the AI/ML talent pool with genuine post-LLM skills (RAG, agents, evals) — this programme…s whole focus. SRC: Pin candidate index, June 2026
Skills You Will Master
Hands-on skills, not slide-deck theory. Every skill below maps to a project you actually build and ship during the programme.
Course Curriculum
4 structured modules — theory, hands-on labs and a weekly shipped project — plus supplementary deep-dives. 8 weeks, live cohort pace at 6 hrs/week.
01 Foundations — Neural Networks & Vision · Weeks 1–2
- Neural networks with TensorFlow / Keras
- Project: digit classifier (MNIST)
- CNNs and transfer learning
- Project: pneumonia detection model
02 NLP & Transformers · Weeks 3–4
- NLP fundamentals and text embeddings
- Project: sentiment classifier
- Transformers with Hugging Face
- Project: resume summariser
03 GenAI Systems — RAG & Multi-Agent · Weeks 5–6
- Prompting strategies and RAG system design
- Project: Q&A bot over documents
- Multi-agent system architecture
- Project: web research agent
04 Integrated Capstone — Deployed AI Assistant · Weeks 7–8
- Design a smart agent for a specific problem or use case
- Integrate search, memory and multi-source reasoning
- Build a real-time conversational interface
- Deploy as a live, user-ready web application
05 Value-Add Modules & Case Studies · Throughout
- Prompt Engineering Deep-Dive — zero-shot to few-shot, refinement, prompt-quality evaluation
- Vector Database Fundamentals — FAISS internals, indexing strategies, hybrid search
- Case study: RAG chunking strategy tradeoffs — fixed-size vs. semantic, measured on retrieval quality
- Case study: agent tool-use failure modes — loops, wrong-tool calls, guardrail patterns
Tools & Technologies
Projects — Six Builds and One Capstone
- Digit Classifier (MNIST) — beginner: train a neural network with TensorFlow/Keras; dataset loading, model architecture, training loop, accuracy evaluation
- Pneumonia Detection — intermediate: CNNs and transfer learning on chest X-rays; preprocessing, fine-tuning a pretrained backbone, precision/recall on real medical imaging
- Sentiment Classifier — beginner: a text-embedding pipeline and classification model labelling text positive, negative or neutral
- Resume Summariser — intermediate: a Hugging Face summarisation pipeline condensing unstructured resume text into structured highlights
- Document Q&A Bot (RAG) — intermediate: chunk and index with FAISS, retrieve passages, generate grounded answers; the pattern behind most production GenAI products
- Web Research Agent — advanced: a multi-agent system that plans a research task, calls search tools and synthesises findings into a report
- Capstone: Deployed AI Assistant — advanced: retrieval, memory and multi-step reasoning behind a real-time conversational interface, deployed as a live public web app
Career Roles You Can Target
GenAI / LLM Engineer
AI Product Developer
AI Engineer — Agentic Systems (junior/mid)
Conversational AI / RAG Systems Engineer
Certification — Stated Plainly
- This programme does not lead to a proprietary GenAI certification exam — no industry-standard certifying body exists for “GenAI Engineer”, so we are not inventing a badge
- You receive a SEHVI programme completion certificate after all four modules and the capstone — an in-house record of completion, not third-party accreditation
- What you actually leave with: a deployed, linkable capstone plus a GitHub portfolio of 7 shipped projects
- Optional and pursued separately: AWS Certified Machine Learning – Specialty, Microsoft Azure AI Engineer Associate — neither is issued by or included in this programme
Who This Is Actually For
- Good fit: web and product developers who already code and want to specialise in LLM systems
- Good fit: data practitioners moving from models to deployed agents
- Good fit: you want a live, linkable deployed project rather than a certificate
- Think twice: zero programming background — this assumes working Python going in
- Think twice: you want breadth across the full ML lifecycle — that is the 12-week AI & ML track, not this one
- Think twice: you have under 6 hrs/week available — this is live-paced, not self-serve
Frequently Asked Questions
Can this really be covered in 8 weeks?
At 6 hrs/week live that is roughly 48 contact hours plus project time outside sessions — enough to build and ship the six listed projects and a working capstone, not enough to make you a research-level ML scientist. Treat it as applied engineering depth.
Do I need coding experience?
Yes. This programme assumes working Python going in. It is not a low-code programme — if you need Python fundamentals first, budget that time separately before week 1.
Is agentic AI hype, or is the demand real?
Agentic-AI postings grew 280% year on year per Final Round AI…s 2026 data — that is real, and it is the fastest-growing slice of AI hiring. The caveat: it is still a specialisation on top of general AI/ML fundamentals, which is why this track spends weeks 1–4 on neural networks, vision and NLP before agents in weeks 5–6.
Is there placement support?
This page makes no placement-rate or guarantee claim, because we do not have verified data for this programme to back one. Our counsellors will tell you exactly what support is available for your cohort.
What are the prerequisites?
Working Python (functions, classes, basic OOP) and comfort with the command line. Basic familiarity with how a neural network trains helps but is not mandatory — Module 01 reintroduces it applied to a real project.
What training modes are offered?
Live online, cohort-based, 6 hrs/week for 8 weeks. Sessions are recorded for review; confirm the current recording-access policy with your programme coordinator, since it can vary by cohort.
What if I miss a live session?
Recorded sessions are the fallback, but this programme is project-paced rather than lecture-paced — missing the live lab means catching up on that week…s project before the next module builds on it. Budget for that if your schedule is unpredictable.
Get Course Details
Our counsellor will contact you within 30 mins with the syllabus, batch dates & fees.
Call a counsellor