Prompting Is Not Enough
Prompt templates are useful, but production workflows need schema control, retries, validation, review flags and failure handling.
Production-focused GenAI engineering
AI Engineering for BFSI: Build Production-Aware GenAI, RAG And Agentic AI Systems
A hands-on GenAI, RAG and Agentic AI program for technical professionals who want to build real BFSI-style and enterprise AI workflows with evaluations, guardrails, memory, multi-agent systems, deployment and cloud-ready architecture.
Recorded theory and guided hands-on labs
Live classes focused on real project implementation
BFSI use cases across KYC, compliance, banking, insurance and market intelligence
Capstone with React, FastAPI, agents, RAG, evals, guardrails and cloud deployment
Program Fee: Rs. 40,000
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Input
Business Problem
Core Architecture
LLM / RAG / Agent Workflow
RetrievalReasoning
Tools
Memory
Guardrails
Evaluation & Human Review
Scorecards, HITL, Metrics
Output
Deployed Business Application
The gap
Why Most GenAI Learning Fails Experienced Professionals
Many GenAI courses teach prompts, tools and small demos. But real business systems fail for different reasons: unreliable outputs, weak retrieval, poor evaluation, unsafe tool access, missing guardrails and no deployment path.
Chatbots Are Not The Final Goal
Most enterprise AI value comes from workflows: extraction, research, summarization, decision support, review, escalation and reporting.
RAG Without Evaluation Is Risky
A RAG system is not reliable just because it returns answers with sources. Retrieval quality, grounding, citation quality and failed cases must be measured.
Agents Break Without Boundaries
Agents need tool permissions, memory design, cost control, human approval, observability and guardrails before they can be trusted in business workflows.
Notebooks Do Not Equal Deployment
A useful AI project needs APIs, frontend integration, environment configuration, logging, testing and cloud-readiness.
Project outcomes
BFSI GenAI Projects You Will Build
The program is structured around practical AI systems, not isolated theory. Each project connects GenAI concepts to a realistic business workflow with implementation, review and production-readiness thinking.
KYC Document Extraction Engine
1- Problem
- KYC forms and onboarding documents often contain noisy, incomplete and inconsistent information.
- Build
- A structured extraction workflow that converts free-form KYC text into validated JSON with missing-field checks and manual-review flags.
Prompt engineeringStructured outputsJSON schemasValidationAuditability
Conversational Core Banking Database Agent
2- Problem
- Business users need answers from banking data, but direct database access creates security and correctness risks.
- Build
- A controlled Text-to-SQL assistant that converts business questions into safe, read-only SQL and summarizes results in business language.
Tool callingSQL validationAgent boundariesSensitive data handlingFollow-up context
Regulatory Compliance And Policy RAG Agent
3- Problem
- Compliance and underwriting teams cannot rely on unsupported AI answers.
- Build
- A RAG assistant that answers policy questions from approved documents with source references, confidence notes and manual-review triggers.
EmbeddingsChunkingVector databasesMetadataGrounded answers
Client Onboarding And Contract Generation Agent
4- Problem
- Client onboarding documentation is repetitive but cannot be fully automated without controls.
- Build
- An agentic workflow that collects client inputs, asks follow-up questions, remembers details and waits for human approval before final drafts.
MemoryHuman-in-the-loopGuardrailsControlled generationReview checklists
BFSI Market Intelligence And Due Diligence Agent
5- Problem
- Analysts need to combine internal notes, public signals, assumptions and risk factors into concise decision briefs.
- Build
- A due-diligence assistant that separates evidence, assumptions, unknowns and confidence levels while producing an executive-ready brief.
Research workflowsEvidence separationSource trackingAssumption management
Omnichannel Wealth Management Prospecting Agent
6- Problem
- Personalized outreach in wealth management must be relevant, accurate and compliant.
- Build
- A multi-agent workflow where one agent researches the prospect, another drafts outreach and a reviewer checks tone, claims and compliance risk.
Multi-agent rolesReviewer agentsCompliance reviewPersonalization boundaries
Voice Banking FAQ Agent
7- Problem
- Voice AI needs low-latency responses, approved knowledge sources and escalation paths.
- Build
- A basic voice FAQ agent that converts speech to text, answers from approved content and converts responses back to speech with escalation handling.
Speech-to-textText-to-speechVoice pipelineGrounded responsesEscalation
Complete Stock Market Analytics Platform
8- Problem
- AI research tools often remain as disconnected scripts and notebooks.
- Build
- A deployable stock-market analytics platform combining React, FastAPI, multi-agent research, RAG, memory, evals, guardrails and cloud deployment.
Full-stack GenAIMulti-agent systemsRAG integrationEvaluation workflowsDeployment
Learning model
How The AI Engineering Course Works
The program separates concept learning, guided implementation and live project work so that classroom time is spent on architecture decisions, debugging, tradeoffs and production thinking.
Recorded Theory
Core concepts, system design patterns, production risks, tool choices and architecture decisions are explained through recorded modules that learners can revisit anytime.
- Transformers and LLM behavior
- Prompt engineering and hallucinations
- RAG architecture
- Agent design
- Evals and guardrails
Recorded Hands-On Labs
Guided implementation videos help learners practice the building blocks before live project sessions.
- Calling LLM APIs
- Creating structured outputs
- Building RAG pipelines
- Connecting vector databases
- Building agents with tools
Live Project Classes
Live sessions focus on real use cases, implementation decisions, debugging, project reviews and production-readiness discussions.
- KYC extraction workflow
- Core banking database agent
- Compliance RAG assistant
- Market intelligence agent
- Capstone implementation
Curriculum
Curriculum Built Around Production AI Systems
The curriculum moves from engineering foundations to GenAI workflows, RAG, agents, BFSI use cases, evaluations, multi-agent systems, tool protocols, voice agents and a full-stack capstone.
Modules
- Module 0: Python for GenAI Engineering Readiness
- Module 1: AI, ML, DL, GenAI and Agentic AI
- Module 2: Prompt Engineering and Structured Outputs
What learners cover
- Python setup and practical scripting
- LLMs as probabilistic systems
- Prompt components and hallucinations
- JSON schemas and structured outputs
- Prompt retries and production practices
Hands-on direction
- Python data analyst mini project
- LLM API calls
- KYC structured extraction
- Client onboarding draft generation
Capstone
Complete Stock Market Analytics Platform
The capstone connects the full program into one deployable AI product workflow. Learners combine frontend, backend, agents, RAG, memory, evaluations, guardrails and deployment into a working stock-market analytics platform.
The point is not just calling an LLM API. The capstone makes system boundaries, data flow, review, reliability and deployment visible.
01
User Request
02
React Frontend
03
FastAPI Backend
04
Multi-Agent Research Workflow
05
RAG Over Documents And Notes
06
Memory, Evals And Guardrails
07
Human Review
08
Analyst-Style Report
09
Cloud Deployment
Fit
Who This Program Is For
Designed For
- Developers who want to build real GenAI applications beyond API calls
- Data analysts and data scientists applying GenAI to business workflows
- ML engineers and AI engineers who want production-oriented RAG and agent patterns
- Tech leads evaluating how GenAI fits into enterprise systems
- BFSI, FinTech, insurance, compliance, lending, risk and analytics professionals
- Professionals with 2-3+ years of experience who want hands-on depth
This Is Not The Right Program If You Want
- A beginner-only Python course
- A passive video-only learning experience
- A collection of prompt tricks
- A no-code AI tools workshop
- A certificate without serious project work
- Hype around AI without implementation discipline
Tools
Tools And Technologies You Will Work With
The course uses modern GenAI engineering tools, but the focus is on transferable architecture patterns: prompting, retrieval, agents, evals, guardrails, APIs, deployment and review workflows.
Programming And App Development
Python
FastAPI
Streamlit
ReactLLM And Agent Development
OpenAI APIs
Anthropic / Claude
LangChain
LangGraph
CrewAIAutoGen patterns
LiteLLMTool callingRAG And Data
EmbeddingsVector databasesDocument loadingChunkingMetadataSource citations
Evaluation And Production Readiness
Golden datasetsRAG scorecardsAgent evaluation criteriaGuardrailsHuman reviewLogging
Emerging Interfaces
MCP conceptsA2A conceptsVoice agentsAI-assisted coding workflows
Tools may vary by cohort based on availability and industry relevance. The course is designed around architecture and implementation patterns, not dependency on one vendor.
Deliverables
What Is Included In The BFSI AI Engineering Program
The program package is designed to make the Rs. 40,000 fee understandable through reusable content, live implementation depth and a capstone project path.
- Recorded concept modules
- Recorded hands-on implementation walkthroughs
- Live project-focused classes
- BFSI and enterprise use case implementations
- Capstone project build
- Project templates and starter workflows
- Prompt and evaluation checklists
- RAG and agent production-readiness checklists
- Brochure and detailed curriculum access
- Guidance on cloud deployment readiness
Program Fee
AI Engineering for BFSI Course Fee
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Enrollment
Contact advisor for availability
Includes recorded theory, recorded hands-on labs, live project classes, use case implementation and capstone project work.
Designed for professionals who want practical GenAI engineering depth and are ready to build serious projects.
Brochure
Download The Detailed Program Brochure
Get the complete curriculum, project list, learning model, capstone details and program structure in one PDF.
FAQ
Frequently Asked Questions
The program includes a Python readiness module, but it is not positioned as a beginner programming course. It is best suited for learners with some technical background who want to build GenAI systems seriously.
Build AI Systems That Survive Real Business Constraints
If you want to move beyond prompt demos and build GenAI systems with retrieval, agents, evaluations, guardrails, memory and deployment readiness, this program is designed for that path.