About Course
About AWS Generative AI Development
Madurai's IT zone is small but growing, and the larger driver of AWS work is the city's healthcare and export sector. Hospitals and diagnostic chains are building DPDP-compliant data handling on cloud, and engineering exporters need documented security governance to hold European buyers.
Both sectors have very little internal platform capability, so graduates who can build a governed data store find themselves on client-facing work within months rather than years.
This Madurai page carries the same 12-week, 72-hour AWS Generative AI Development 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 AWS Generative AI Development training (72 hours) covering Amazon Bedrock, RAG, agents, evaluation, guardrails and token economics. Live online and classroom.
Why Learn AWS Generative AI Development
Hospitals and diagnostic chains are building DPDP-compliant data handling with very little internal capability, so new engineers get client-facing work early.
Engineering exporters need documented security governance to hold European buyers, and that work spans the whole contract lifecycle.
Retail and distribution groups handling customer loyalty data are an under-served local market for cloud and privacy engineers.
AWS hiring sectors in Madurai
- Hospitals and diagnostic networks
- Engineering exporters
- IT services and support operations
- Retail and distribution groups
Demand drivers behind Madurai cloud hiring
- DPDP Act obligations in healthcare data handling
- Security governance required by European buyers
- Customer audits across export supply chains
- Local IT services cloud adoption
Who Should Join This Course
- Backend Developers — Adding LLM features to products they already own
- Software Engineers — Needing evaluation and cost discipline around models
- Full-Stack Developers — Shipping AI features with a real UI and a real bill
- Fresh Graduates — Targeting generative AI roles without a research background
- Technical Leads — Deciding what is safe to put in front of customers
- Consultants — Scoping LLM work for clients without over-promising
Prerequisites
No prior AWS experience required unless the course page states otherwise. Basic IT familiarity, comfort with a web console and command-line basics are enough to start; AWS itself, its vocabulary and its operating discipline are taught from first principles.
Learning Objectives
- Choose a foundation model against a written requirement, not a benchmark headline
- Build retrieval-augmented generation that cites and stays on source
- Design agents and tool use without letting a model roam freely
- Evaluate open-ended output with a method that produces a number
- Install guardrails that catch the failures that actually matter
- Model latency, token cost and capacity before shipping a feature
- Handle prompt injection, data leakage and abuse paths
- Ship a generative AI feature with monitoring and a cost ceiling
AWS Services and Tools Covered
Amazon Q Developer, Amazon Bedrock, Amazon OpenSearch Service, Amazon Titan, AWS Lambda, Amazon Kendra together with evaluation and guardrail practice, prompt-injection testing method, and the ISO 27001 and DPDP obligations that apply whenever a model reads customer documents.
Career Opportunities After This Programme
- Generative AI Developer
- LLM Application Engineer
- AI Engineer (Bedrock)
- Solutions Engineer (GenAI)
- Prompt Engineer
- Applied AI Developer
Hands-on Practice and Capstone
Capstone: Take a written product brief, choose a foundation model against it rather than a benchmark headline, build a retrieval-augmented feature over a real document set with citations, add constrained tool use with an auditable action set, write an evaluation suite and score the feature honestly including its refusal behaviour, install guardrails and a prompt-injection test, then ship it with monitoring, a budget alert that cannot be exceeded and a rollback you have rehearsed.
Production scenario: A document Q&A feature answers confidently from the wrong source and costs more than the team budgeted for. Rebuild the retrieval path, separate retrieval quality from generation quality in the evaluation, fix the citation so answers show their source, and redesign the cost model so a spike in traffic cannot produce an unexpected bill.
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. Certifications mapped: SkilBrill Course Completion Certificate.
Is classroom training available in Madurai? Classroom batches run at our Thoraipakkam centre in Chennai. Learners in Madurai 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 cloud engineers and architects, every module produces a portfolio artefact 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 cloud and platform roles
- LinkedIn profile rewrite and recruiter outreach
- Mock interviews covering architecture, operations and troubleshooting scenarios
- Capstone project walkthrough preparation
- Placement assistance with 200+ hiring partners
- Typical salary band after completion: ₹8 – 25 LPA
Frequently Asked Questions
What is AWS Generative AI Development training at SkilBrill?
It is the engineering track for building applications on foundation models with Amazon Bedrock: retrieval over your own documents, agents with constrained tool use, evaluation that produces a number, guardrails, and the token economics behind every call. SkilBrill runs it over 12 weeks and 72 hours.
How is this different from AWS AI Fundamentals?
AI Fundamentals teaches you to judge and scope AI use cases using managed services, with no coding required. This is the development track: you build, evaluate and ship LLM features in code. Take AI Fundamentals first if you are scoping rather than building.
How is this different from AWS Machine Learning?
Machine Learning is predictive modelling and MLOps: training, deployment, drift and retraining of classifiers and regressors. This course is about applications on foundation models — retrieval, agents, evaluation of open-ended text and token cost. The evaluation problems are genuinely different.
Do I need a maths or research background?
No. You should be able to write code in at least one language. The course teaches how these models behave, how to evaluate them and how to ship them safely, and deliberately does not go into model training from scratch — that is the AWS Machine Learning track.
What is the duration and fee?
12 weeks, three sessions a week of two hours each, totalling 72 classroom hours. Fees are ₹55,000 – ₹85,000 depending on batch and mode, placing it in the premium band.
What jobs can I get after this course?
Generative AI developer, LLM application engineer, AI engineer on Bedrock, solutions engineer for AI products and prompt engineer. In India these typically start at ₹8–25 LPA, with product-company roles at the higher end.
Is classroom training available in Madurai?
No. SkilBrill's classroom runs only in Chennai. Learners in Madurai 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 Madurai?
Learners in Madurai typically target a mix of MNCs and startups such as HCL, TCS. The programme's placement support applies equally to Madurai learners.