Backend Developer
Course in Hyderabad

1. 1

   Python & Technical Foundations

   Module 1 · 30 Hrs

   Everything else stands on this. Concentrate on Python, functions and classes, error handling, virtual environments, Git, HTTP and REST, JSON and basic tests, and set up a GitHub portfolio early.

   What you study

   • Python programming fundamentals — syntax, data structures and control flow
   • Functions and object-oriented programming in Python
   • Error handling and writing resilient code
   • Virtual environments and dependency management
   • Git and GitHub version control workflows
   • HTTP fundamentals and REST APIs
   • Basic automated testing

   Tools you use

   PythonGitGitHubPostmanREST APIs

   Hands-on lab

   Call and test REST APIs using Postman, and parse JSON responses in Python.

   See the full module →
2. 2

   Python Backend Development with AI

   Module 2 · 40 Hrs

   This is the heart of the role. Give it your full effort: FastAPI design, both database types, Redis queues, JWT and access control, testing, Docker, CI/CD and cloud deployment, ending with the backend project.

   What you study

   • Advanced Python and modular application architecture
   • FastAPI development and RESTful API design
   • PostgreSQL and MongoDB database fundamentals
   • Redis and background job queues
   • Authentication, authorization and JWT / OAuth
   • API security and role-based access control
   • Testing and test-driven development

   Tools you use

   FastAPIPostgreSQLMongoDBRedisPytest

   Hands-on project

   Backend API Service. Build and deploy a secure, production-style REST API with FastAPI, PostgreSQL/MongoDB, authentication and Docker.

   See the full module →
3. 3

   Generative AI & LLM Application Engineering

   Module 4 · 40 Hrs

   Backend teams now add language model features. Concentrate on calling LLM APIs, structured outputs, function calling, and pgvector search inside PostgreSQL, since these fit naturally into services you can already build.

   What you study

   • LLM APIs and prompt engineering
   • Structured outputs and function calling
   • Embeddings and vector databases
   • Document ingestion and chunking
   • Retrieval-Augmented Generation (RAG)
   • PostgreSQL and pgvector

   Tools you use

   LLM APIsPostgreSQLpgvectorFastAPI

   Hands-on lab

   Generate and store embeddings in a vector database using pgvector.

   See the full module →
4. 4

   MLOps, LLMOps & AI Platform Engineering

   Module 6 · 30 Hrs

   Backend work does not end at release. Focus on CI/CD, regression tests, tracing, cloud IAM and secrets, scaling and rollbacks, which are the habits that keep a service dependable.

   What you study

   • Regression testing for AI systems
   • CI/CD for AI systems
   • Tracing and observability
   • Cloud IAM and secrets management
   • Scaling and rollbacks
   • Cost and latency management

   Tools you use

   GitHub ActionsDockerCloud PlatformMonitoring Tools

   Hands-on lab

   Set up a CI/CD pipeline that tests and deploys an AI system automatically.

   See the full module →
5. 5

   AI Solutions Engineering

   Module 7 · 40 Hrs

   Real services connect to other systems and hold private data. Use this module for API and system integration, security and PII handling, UAT, and writing documentation your team can follow.

   What you study

   • Data and system integration
   • API integration
   • Security and PII handling
   • Solution design and prototyping
   • UAT and deployment
   • Cost estimation and technical documentation

   Tools you use

   PythonFastAPIREST APIsPostgreSQLDocker

   Hands-on lab

   Run UAT on a prototyped solution and prepare it for deployment.

   See the full module →

What the programme covers for this role. This role course covers Python backend and AI-application backend work: FastAPI, databases, security, queues and deployment. It does not teach Java, Node.js, Go, Kubernetes or message brokers such as Kafka, so those would be separate learning for teams that use them.