About Course
About LLM Application Development Training
Bangalore's product and startup density is the largest single driver of AI hiring in India, and the work sits inside engineering rather than beside it. Companies here are not asking whether to use a model, they are asking why their evaluation set is small, why retrieval returns the wrong document and what a feature costs per thousand requests. That is a different class of problem from adoption, and it is why the city pays for engineers who can measure rather than demonstrate.
What distinguishes Bangalore is how quickly responsibility lands on individuals. An engineer here is usually answering for latency, cost, evaluation and safety across several services at once, which is why depth travels further in this city than a single certificate. Global capability centres run their parent's AI programmes from here too, so governance and platform roles carry group-level scope unusually early.
This Bangalore page carries the same 12-week, 72-hour LLM Application Development Training syllabus, faculty and assessment as the master programme, delivered live online and in hybrid format, with classroom batches at our Thoraipakkam centre.
Course Overview
12-week LLM Application Development training (72 hours) covering RAG, chunking and embeddings, vector databases, reranking, function calling, streaming and production evaluation. Classroom, Live Online and Hybrid.
Why Learn LLM Application Development Training
Bangalore product teams fold evaluation into delivery, so engineers who can show a measured improvement rather than an impressive demo are hired ahead of candidates with certificate lists only.
Global capability centres build their parent company's AI and data platform from Bangalore, creating platform and governance roles with group-level scope from an early stage.
Fintechs scaling lending and payments must satisfy RBI requirements on model explainability, and they compete hard for people who can document what a model does.
AI and data hiring sectors in Bangalore
- Product companies and SaaS exporters
- AI startups and applied research teams
- Global capability centres
- Fintech, consumer platforms and healthtech
Demand drivers behind Bangalore AI and data hiring
- Enterprise procurement demanding evaluation evidence before an AI feature is approved
- Retrieval, grounding and cost problems appearing in production rather than demos
- Parent-owned AI and data platforms run out of capability centres
- GDPR and DPDP obligations on EU and Indian user data in model pipelines
Who Should Join This Course
- Backend Developers — Adding a retrieval-backed answer to an existing product
- Data Engineers — Moving from pipelines into the serving layer
- Full-Stack Developers — Building a chat or search interface over company documents
- Machine Learning Practitioners — Moving from training notebooks to deployed apps
- Fresh Graduates — Targeting the generative AI engineering track
- Solution Architects — Designing an LLM feature that has to survive production
Prerequisites
Working Python is required: functions, dictionaries, JSON, basic error handling and comfort reading someone else's code. Some exposure to SQL or an API integration helps in the vector store material. No machine learning or deep learning background is assumed, and the embedding and evaluation theory is taught from first principles.
Learning Objectives
- Design a RAG pipeline whose chunking and retrieval you can justify
- Choose and configure a vector database for the scale and latency you need
- Improve answer quality by fixing retrieval before touching the prompt
- Implement function calling and streaming for a responsive interface
- Build a retrieval evaluation set and measure recall independent of the model
- Control cost and latency with caching, model routing and prompt budgets
- Add guardrails in code for injection, refusal and citation enforcement
- Ship a documented, monitored LLM application with a rollback path
What the Syllabus Covers
Each module is assessed against published LLM Application Developer outcomes rather than on memorisation, covering RAG, Vector Databases, Embeddings, Function Calling, Streaming, Retrieval Evaluation together with a chunking strategy argued from the corpus, a hybrid search and reranking pipeline, function calling with validated schemas, and a retrieval evaluation suite that reports recall before it reports answer quality.
Career Opportunities After This Programme
- LLM Application Developer
- Generative AI Engineer
- AI Software Engineer
- RAG Engineer
- Applied AI Developer
- Solutions Engineer (GenAI)
Hands-on Practice and Capstone
Capstone: Ingest a real document corpus with structure-aware chunking, build hybrid retrieval with a reranking step and measure recall against a gold set, generate answers with mandatory citations and a refusal path, add a function-calling tool and stream the response, then instrument tracing, cache the hot path and ship with a retrieval eval suite, runbook and rollback procedure.
Production scenario: The demo answers beautifully on three curated documents and terribly on the real share. The build here starts by proving which half is broken: retrieval metrics show the right chunk was never fetched, so the fix is chunking, hybrid search and a reranking pass rather than a cleverer prompt, and the eval suite is written so that claim can be repeated after every index change.
Course Duration, Mode and Certification
The course runs for 12 weeks (72 hours), three sessions a week of two hours each. Modes: Classroom / Live Online / Hybrid. Certificate issued: SkilBrill Course Completion Certificate. Assessment is against published LLM Application Developer outcomes, and you finish with evidence you can show rather than a pass mark.
Is classroom training available in Bangalore? Classroom batches run at our Thoraipakkam centre in Chennai. Learners in Bangalore join the same live online or hybrid cohort taught by the same faculty, with the same assessment and placement support.
Why Train at SkilBrill
SkilBrill Training Institute in Thoraipakkam focuses on making you employable. Trainers are working AI, data and platform engineers, every module produces portfolio evidence rather than a completion badge, and the placement cell connects eligible candidates with hiring companies. Fee: ₹55,000 – ₹85,000. Enquire on +91 8610964691.
Career and Job Support
- Resume assistance tailored to AI, data and platform engineering roles
- LinkedIn profile rewrite and recruiter outreach
- Mock interviews covering evaluation, pipeline design and system-review scenarios
- Capstone walkthrough and portfolio review preparation
- Placement assistance with 200+ hiring partners
- Typical salary band after completion: ₹8 – 22 LPA
Frequently Asked Questions
What does LLM Application Development training at SkilBrill cover?
Twelve weeks and 72 hours across eight modules: the shape of an LLM application, retrieval and chunking, embeddings and vector stores, generation with tools and structured output, production engineering, retrieval evaluation, applied practice, and a full RAG capstone.
Do I need to know Python?
Yes, working Python. You will write and read application code, so you should be comfortable with functions, dictionaries, JSON and asynchronous requests. No machine learning background is required, and no prior LLM experience is assumed.
How is this different from the Generative AI course?
Generative AI Training is the practitioner and governance side: prompting, evaluation, safety and cost, and it needs no code. This course is the engineering side and builds the application itself. Most people take Generative AI first if they are new to the field, then move to this one to build.
What is RAG and why is it the focus?
Retrieval-augmented generation fetches relevant documents first and hands them to the model, so answers can cite real sources instead of relying on training data. It is the dominant pattern for building on private company content, and retrieval quality is where most RAG projects fail.
Is certification included?
The syllabus follows LLM application developer objectives, so the course doubles as preparation. Vendor credentials are booked through their own programmes, and the SkilBrill completion certificate is issued by us on the capstone.
Which frameworks and vector stores are used?
The course is deliberately not locked to one framework. You work with a mainstream Python SDK for the model calls and build the retrieval layer yourself, then use at least two vector stores so the trade-offs are visible rather than assumed.
What do I leave with?
A working retrieval application with citations, streaming, a function-calling tool, tracing, a retrieval evaluation suite, and the runbook and rollback procedure needed to hand it to a team that has to operate it.
Is classroom training available in Bangalore?
No. SkilBrill's classroom runs only in Chennai. Learners in Bangalore join the same live online / hybrid cohort taught by the Chennai faculty, with proctored labs and the same placement support.
Which companies hire for these skills in Bangalore?
Learners in Bangalore typically target a mix of MNCs and startups such as Infosys, Wipro, Accenture, IBM, Microsoft. The programme's placement support applies equally to Bangalore learners.
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