Skillmaxx Academy
Developer working with an agent graph, application interface and deployment tools

Advanced, live AI engineering programme

Full Stack AI Agent Developer

Design, build, evaluate and deploy production AI agent applications.

Build full-stack agent systems with Python, FastAPI, LangGraph, RAG, PostgreSQL, MCP, evaluation, security and deployment, then ship a portfolio-ready capstone with a working web interface.

Designed for learners with prior programming exposure.

₹69,999

inclusive of GST

MRP ₹1,29,999

You save ₹60,000 (46% off)

EMI starting from ₹7,778/month. Eligibility and terms apply.

View the 12 Builds
144 live hours
Approx. one-year LMS access
Recorded live sessions
12 production-oriented builds
Python + agent engineering
Deployment, evaluation and security

Technical readiness

Confirm that this advanced programme matches your starting point

This is not a no-code beginner course. Learners without Python experience must complete the pre-course Python bridge before the main programme.

Starting level
Prior programming logic or coding exposure is strongly recommended.
Coding
The programme uses Python services, tests, APIs and a Next.js/React interface.
Laptop
16 GB RAM is recommended; a dedicated local GPU is not required.
Practice
Plan eight to twelve hours per week outside live classes.
External costs
Selected model APIs, cloud GPUs, databases and hosting may require separate credits.

Production engineering outcomes

Develop the engineering capability behind reliable AI agents

Move beyond chatbot demos by combining software engineering, retrieval, orchestration, evaluation, security, observability and deployment.

1
Build production-quality Python services
Use typing, Pydantic, async programming, FastAPI, testing, logging, Git and Docker.
2
Choose and integrate language models
Compare hosted and open models for quality, latency, privacy, structured output and cost.
3
Evaluate LLM behaviour systematically
Use versioned prompts, schemas, golden cases, adversarial tests and release thresholds.
4
Engineer reliable retrieval systems
Build ingestion, metadata, hybrid retrieval, reranking, citations and retrieval evaluation.
5
Fine-tune where it creates value
Prepare datasets and compare supervised, LoRA or QLoRA experiments with simpler alternatives.
6
Build stateful tool-using agents
Implement graph state, memory, tools, budgets, retries and stopping conditions.
7
Design human-supervised workflows
Add approvals, escalation, audit events and recovery logic.
8
Ship secure, observable applications
Combine interface, API, database, agent runtime, permissions, evaluation, tracing, CI/CD and deployment.

Built for technical learners

Designed for people ready to engineer complete AI applications

Python and backend developers
Extend software skills into LLM applications, RAG, orchestration and deployment.
Software and data engineers
Build systems around models, retrieval, APIs, state and observability.
AI automation engineers
Move from visual automation into coded architecture, testing and security.
Engineering students with coding experience
Build substantial projects that demonstrate more than API calls and chatbot interfaces.
Technical career switchers
Suitable for learners who understand programming logic and are prepared for sustained coding practice.
Freelancers, consultants and founders
Develop agent products with architecture, evaluation and operating documentation.

This is not a no-code beginner course. Complete the pre-course Python bridge if you have programming experience but limited Python exposure.

Guided engineering builds

Build twelve increasingly complete AI systems

Each build adds another production concern, from typed Python services and evaluation through RAG, fine-tuning, stateful agents, MCP and a deployed full-stack capstone.

Illustrative guided build for Typed Python LLM Service
Engineering buildBuild 1
Typed Python LLM Service
Create a tested FastAPI service with validation, model calls, streaming, logging and API documentation.
PythonFastAPIPydantic

You leave with: A tested service foundation suitable for larger AI applications.

Illustrative project output. Interfaces and platform versions may change.

Illustrative guided build for Multi-Model Benchmark and Router
Engineering buildBuild 2
Multi-Model Benchmark and Router
Compare models for quality, latency, cost, structured output and tool use, then implement routing.
PythonStreamlitModel APIs

You leave with: A measured model-selection workflow rather than a preference-based choice.

Illustrative project output. Interfaces and platform versions may change.

Illustrative guided build for Evaluated Structured-Output Application
Engineering buildBuild 3
Evaluated Structured-Output Application
Build a versioned prompt service with schemas, golden cases, adversarial tests and release thresholds.
PydanticPromptfooDeepEval

You leave with: A release gate that catches output regressions before deployment.

Illustrative project output. Interfaces and platform versions may change.

Illustrative guided build for Vector Ingestion and Search Service
Engineering buildBuild 4
Vector Ingestion and Search Service
Ingest mixed documents, apply metadata and access fields, create embeddings and expose hybrid search.
PythonPostgreSQLpgvector

You leave with: A searchable knowledge layer with metadata-aware retrieval.

Illustrative project output. Interfaces and platform versions may change.

Illustrative guided build for Production RAG Knowledge Assistant
Portfolio buildBuild 5
Production RAG Knowledge Assistant
Build a cited assistant with query rewriting, reranking, abstention, caching, permissions, tracing and evaluation.
RAGGradioLangfuse

You leave with: A grounded assistant whose retrieval and answers can be inspected.

Illustrative project output. Interfaces and platform versions may change.

Illustrative guided build for Supervised Fine-Tuning Experiment
Research buildBuild 6
Supervised Fine-Tuning Experiment
Validate a task dataset, fine-tune a small model, compare with baseline and write a selection memo.
Hugging FaceJupyterLabTRL

You leave with: An evidence-based decision about whether fine-tuning improved the task.

Illustrative project output. Interfaces and platform versions may change.

Illustrative guided build for LoRA or QLoRA Domain Adapter
Research buildBuild 7
LoRA or QLoRA Domain Adapter
Train an adapter, track experiments, benchmark behaviour and publish a model card.
PEFTbitsandbytesHugging Face

You leave with: A reproducible adapter experiment with documented trade-offs.

Illustrative project output. Interfaces and platform versions may change.

Illustrative guided build for Stateful Tool-Using Agent
Agent buildBuild 8
Stateful Tool-Using Agent
Build typed tools, explicit graph state, memory, retrieval, retries, budgets and stopping conditions.
LangGraphLangSmith

You leave with: An inspectable agent graph with bounded, testable behaviour.

Illustrative project output. Interfaces and platform versions may change.

Illustrative guided build for Human-Supervised Operations Agent
Agent buildBuild 9
Human-Supervised Operations Agent
Add risk-based approvals, escalation, audit events, persistence and recovery logic.
LangGraphPostgreSQLLangfuse

You leave with: A durable workflow that pauses safely before high-risk actions.

Illustrative project output. Interfaces and platform versions may change.

Illustrative guided build for Measured Multi-Agent System
Agent buildBuild 10
Measured Multi-Agent System
Coordinate specialists and compare against a simpler single-agent baseline.
LangGraphCrewAIAutoGen

You leave with: A benchmark showing whether added orchestration is actually justified.

Illustrative project output. Interfaces and platform versions may change.

Illustrative guided build for Secured MCP Integration
Security buildBuild 11
Secured MCP Integration
Build an MCP server and client with scoped tools, authentication, secrets and injection tests.
MCP Python SDKOAuthFastAPI

You leave with: A least-privilege tool integration with documented threats and controls.

Illustrative project output. Interfaces and platform versions may change.

Illustrative guided build for Full-Stack Agent Product
Full-stack capstoneBuild 12
Full-Stack Agent Product
Ship a web interface, FastAPI backend, PostgreSQL/pgvector, RAG, tools, memory, approvals, evaluation, tracing, CI/CD and runbook.
Next.jsFastAPILangGraphPostgreSQL

You leave with: A deployed product, architecture case study and operating runbook.

Illustrative project output. Interfaces and platform versions may change.

Your learning platform

Live classes end. Your learning workspace remains available.

Return to the SkillmaxX LMS for approximately one year to review recordings, revisit course material, download working resources and continue improving your guided builds.

Approx. one-year LMS access

Course dashboard

See your course, live sessions and learning access in one place.

1 of 3
Course dashboard in the SkillmaxX learning platform
Structured learning path in the SkillmaxX learning platform
Learning progress in the SkillmaxX learning platform

Live + LMS

How your learning works

1
Prepare in the LMS
Review prerequisites, starter files, required accounts and setup instructions.
2
Learn the concept live
Understand the reasoning, architecture and practical use case before building.
3
Watch the complete demonstration
See the instructor build, test and troubleshoot the workflow.
4
Build alongside the instructor
Implement the workflow using your laptop and the supplied course files.
5
Test and improve
Validate outputs, investigate failures and document your decisions.
6
Revisit and extend
Use recordings and LMS resources to adapt the build for your portfolio.
Munir Suri, Founder and Lead Instructor at SkillmaxX Academy

Your instructor

Learn through architecture, code, testing and deployment evidence

Munir Suri

Founder & Lead Instructor - SkillmaxX Academy

Engineer | IIM Kozhikode alumnus | Former Director, A.T. Kearney | Full Stack Development, IIT Madras | Digital Marketing, ISB Hyderabad

Munir Suri combines management education, transformation experience and formal full-stack development study. The programme connects technical architecture with product decisions, operating constraints and clear documentation.

Sessions use working code, build walkthroughs, evaluation results and failure analysis so learners understand why an implementation is reliable, not merely that a demo ran once.

Munir leads the software-engineering, agent-architecture, integration and capstone work through live implementation and review.

Five engineering phases

Detailed curriculum

Progress from Python services and model integration through retrieval, fine-tuning, stateful agents, security and a deployed full-stack product. 144 structured learning units are included.

Phase 1 · Software, models and evaluation

Phase 2 · Retrieval engineering

Phase 3 · Model adaptation

Phase 4 · Stateful and multi-agent systems

Phase 5 · Security, full stack and operations

Production stack

Use a focused core stack, then compare specialised alternatives

The programme goes deep on Python services, LangGraph, PostgreSQL, evaluation and deployment. Other frameworks are compared where they sharpen an engineering decision.

Core production stack

P
PythonProduction application and agent engineering
FA
FastAPI and PydanticTyped APIs, schemas and validation
PA
pytest and RuffTesting and code quality
PA
PostgreSQL and pgvectorApplication data, state and vector retrieval
L
LangGraphPrimary stateful agent framework
HM
Hosted model APIsModel selection and integration
LO
Langfuse or LangSmithTracing, evaluation and observability
DA
Docker and GitHub ActionsContainers, CI/CD and reproducible deployment
NJ
Next.js and ReactFull web application interface for the capstone

Specialised and compared components

Models, datasets and fine-tuning

Hugging Facesentence-transformersTRLPEFTbitsandbytes

Evaluation and state

RagasRedisOpenTelemetryMLflow

Agent frameworks compared

LlamaIndexCrewAIAutoGenPydanticAI

Vector platforms compared

PineconeQdrantFAISS

Rapid interfaces and deployment

StreamlitGradioRenderRailway

Protocols and security

MCP Python SDKOAuthVaultOpenTelemetry

Frameworks and model providers evolve. The programme preserves durable architecture around state, tools, retrieval, evaluation, approvals, security and observability.

A complete engineering programme

What your course fee includes

144 hours of live engineering instruction
Twelve production-oriented guided builds
Session recordings
Approximately one year of LMS access
Starter repositories, datasets, test assets and architecture briefs
Capstone, portfolio and completion-certificate guidance

Requirements and costs

Confirm your technical readiness before joining

Prior programming logic or exposure to Python, JavaScript, Java, C++ or another language is strongly recommended.

Learners without Python experience must complete the pre-course Python bridge before the main programme.

Comfort with files, a terminal, APIs, JSON and Git is helpful.

A laptop with at least 16 GB RAM is recommended; a dedicated GPU is not mandatory.

Plan eight to twelve hours of practice per week outside live classes.

Be prepared to read code, debug failures, write tests and document projects.

Third-party tool costs
Selected model APIs, cloud GPU labs, vector databases and deployment platforms may require learner-managed accounts and separate credits. Required and optional costs are identified before each lab, with local or free alternatives where practical.

Course completion

SkillmaxX completion certificate

Complete the published learning, practical work and course requirements to receive a SkillmaxX Academy completion certificate.

The certificate records completion of the programme. Your guided builds and documented project work provide the practical evidence behind it.

Sample SkillmaxX Academy completion certificate

Course fee

Build production AI agent applications

Course fee

₹69,999

inclusive of GST

MRP ₹1,29,999

You save ₹60,000 (46% off)

EMI starting from ₹7,778/month. Eligibility and terms apply.

Your fee includes 144 live hours, twelve engineering builds, recordings, approximately one year of LMS access, project repositories and assets, capstone guidance and a completion certificate for eligible learners.

EMI options may be available for eligible learners. No payment is required to request a technical course guidance call.

Confirm technical readiness, the Python bridge, schedule and external lab costs during your guidance call.

Questions before joining

Frequently asked questions

Move beyond chatbot demos and build production AI systems

Confirm your technical readiness, understand the pre-course bridge and see how the twelve builds lead to a deployed full-stack agent product.

Prior programming exposure and sustained weekly practice are required.