Step 3 of 3 · Engineer · Advanced

Enterprise AI Engineering: From Prototype to Production

Our flagship intensive. Take an AI application through the parts enterprises actually care about: architecture, evaluation, security, deployment, observability, cost and governance, applied to a regulated BFSI use case and reviewed like a real design review.

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14 hours live6 hands-on labsIndustry use caseLive & mentor-guided

Duration2 days · 14 hours live

FormatLive online · hands-on

Fees & datesShared on WhatsApp

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The industry use case

One real problem, start to finish

Every concept in this workshop is applied to the same realistic scenario, so you learn how the pieces fit together, not just what they are.

The company

An insurance company (BFSI) automating claims queries under strict compliance rules

The problem

A promising AI prototype exists, but security, audit, cost and reliability concerns keep it out of production.

What you build

A hardened claims assistant with evaluation gates, prompt-injection defences, tracing, cost controls and an enterprise architecture blueprint you can reuse at work.

Who it's for

Is this workshop for you?

Senior developers & tech leadsOwn the engineering of AI features in production.

Solution & enterprise architectsDesign AI systems that pass security and architecture review.

ML, data & platform engineersAdd LLMOps, evaluation and observability to your toolkit.

Engineers who finished BuildTake your application from prototype to production.

Before you join

Helpful: the Build workshop or experience with RAG or agents (not required)
Helpful: familiarity with REST APIs, Git and Docker

Open entry: anyone can join. Prefer a gentler start? Try Build.

This is not

An introduction to AI
A framework tour
A slide-only architecture talk

Curriculum

2 days · 11 sessions · 6 hands-on labs

Day 1

Enterprise architecture, evaluation and security

Enterprise AI system design
- Requirements, non-functional requirements and service boundaries
- Data flows and architecture decision records
Advanced RAG and agent patternsLab
- Routing, tool permissions and deterministic guards
- Human-in-the-loop where the risk demands it
Evaluation engineeringLab
- Golden datasets, retrieval metrics, groundedness and task success
- Regression tests and acceptance thresholds as release gates
Security for AI systemsLab
- Authentication, authorisation, secrets and tool-access control
- Prompt injection, data exposure and a hands-on red-team lab
Design review
- Find the failure modes in a proposed enterprise AI architecture
- Present and defend your fixes

By the end of Day 1: Evaluation suite and security hardening for the claims assistant.

Day 2

Deploy, operate and govern

Serving and deploymentLab
- APIs, containers and environment separation
- CI/CD with evaluation gates
ObservabilityLab
- Traces, logs, latency, token usage and failures
- Following an agent's execution path in a tracing tool
LLMOps
- Versioning prompts, models and datasets
- Release workflows, monitoring and rollback
Cost and performance engineeringLab
- Caching, model routing and context management
- Concurrency and capacity planning
Governance and compliance
- Audit trails, data handling and approval boundaries
- Operational ownership, and DPDP Act awareness for India
Capstone review
- Your enterprise-ready architecture and implementation plan
- Reviewed like a real architecture board

By the end of Day 2: Deployment-ready claims assistant with an observability plan and an enterprise architecture blueprint.

Tools & stack

PythonLangGraphFastAPIDockerTracing (Langfuse / OpenTelemetry)Evaluation frameworksCloud deployment

Inside the labs

See what a lab looks like

Real recordings from our hands-on labs: code, running apps and measured results.

RAG lab

A documentation assistant, built end to end

A lab project: FastAPI backend, Chroma vector store and Llama 3 via Groq, answering questions about LangChain documentation.

RAGFastAPIChromaLlama 3

RAG evaluation lab

Measuring answer quality, not guessing

A lab notebook: a local FAISS knowledge base, a RAG pipeline on Groq LLMs, and RAGAS metrics to evaluate the answers. Shown at 2.5× speed.

RAG evaluationRAGASFAISSGroq

Outcomes

After two days, you will be able to…

Design an AI system that passes an enterprise architecture and security review
Build evaluation into the release process instead of testing by feel
Defend against prompt injection and data exposure
Deploy, trace and monitor an AI application in production
Control cost and latency with caching, routing and context management
Put governance, audit and human approval where they belong

You leave with

Deployment-ready application repository

Evaluation & security checklist

Observability plan

Enterprise AI architecture blueprint

Verifiable certificate of completion

Your mentor

Taught live by a practising AI engineer

Dr. Gajanan

Lead Mentor · Machine Learning, Generative AI & Agentic AI

14+years in ML & AI

ML→GenAI→Agentsthe full arc of modern AI

Designs the curriculum and leads the hands-on labs, helping participants think through problems the way an AI engineer does instead of memorising tools.

Machine learningGenerative AIAgentic AI systemsPython for AI

FAQ

Questions about this workshop

Can I join without the earlier workshops?

Yes. Anyone can join any workshop. If you are new to RAG and agents, Build is a helpful step first, but it is not required.

Can my company send a team?

Yes. We run this in-house for engineering teams, scoped around your stack and one of your real use cases. Request a proposal and we will share a quote.

Which cloud do we deploy to?

We use container-based deployment that maps to AWS, Azure or GCP, and discuss the differences.

Is client or employer IP discussed?

No. Sessions use realistic synthetic data and general industry knowledge, never proprietary client or employer material.

Reserve your seat

Engineer: Enterprise AI Engineering

Share your details. Cohort dates and a secure Razorpay payment link arrive on WhatsApp. Nothing is charged until you confirm.

Fees & datesOn WhatsApp, within minutes

Duration2 days · 14 hours

Full refund if you cancel 48+ hours before the start
Course materials and a verifiable, LinkedIn-ready certificate
Planning more than one workshop? Talk to us

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Takes under a minute.

Stop building AI demos. Start engineering AI systems.

2 days · live online · hands-on

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