Python AI Backend Engineering
Go from backend developer to AI-powered system builder in 16 weeks.
- Duration
- 16 weeks
- Duration
- Sessions
- 24
- Sessions
- Labs
- 16
- Labs
- Projects
- 3
- Projects
What You'll Be Able To Do
After completing this course, you will confidently:
- Build production-grade REST APIs with FastAPI including authentication, validation, and structured error handling
- Design and query relational databases with PostgreSQL and manage schema evolution with Alembic migrations
- Integrate OpenAI and open-source LLMs into backend services with proper error handling and cost controls
- Build retrieval-augmented generation pipelines with vector databases for accurate, grounded AI responses
- Design embedding pipelines that chunk, embed, and index documents for semantic search
- Implement LangChain agents with tool use for multi-step reasoning and task automation
- Evaluate AI system quality using automated metrics, human feedback loops, and regression testing
- Deploy AI-powered backends with Docker, implement rate limiting, and monitor token usage and latency
What You'll Build
Real portfolio projects that showcase your skills to employers.
AI Document Q&A System
Build a RAG-powered document Q&A API that ingests PDFs and Markdown files, chunks and embeds them, stores vectors in ChromaDB, and answers natural language questions with source citations.
Interview value:
RAG systems are the most asked-about AI architecture in backend interviews. This project shows you can build one end-to-end.
Semantic Search API
Design a semantic search service that indexes product catalogs or knowledge bases using embeddings, supports hybrid search (keyword + semantic), and returns ranked results with relevance scoring.
Interview value:
Semantic search is replacing keyword search across the industry. This project demonstrates vector search architecture skills.
Intelligent Task Automation Agent
Build an AI agent that interprets natural language instructions, plans multi-step actions, calls external APIs and tools, and reports results. Includes guardrails, cost controls, and human-in-the-loop approval.
Interview value:
AI agents are the frontier of LLM applications. This project shows your ability to build autonomous systems with proper safety controls.
Course Curriculum
16 weeks of structured, hands-on learning.
1Python Advanced Patterns for Backend
- Type hints, Pydantic models, and runtime validation
- Decorators, context managers, and generators
- Async/await fundamentals and the asyncio event loop
- Project structure, dependency management, and linting
2FastAPI Fundamentals
- Application structure, routing, and dependency injection
- Request validation, response models, and status codes
- Middleware, CORS, and error handling
- OpenAPI documentation and Swagger UI
3PostgreSQL & SQLAlchemy
- Relational database design and normalization
- SQLAlchemy async models and relationship patterns
- Alembic migrations and connection pooling
- Query optimization, indexes, and EXPLAIN analysis
4Authentication, Redis & Caching
- JWT authentication and OAuth2 integration
- Redis data structures and caching strategies
- Rate limiting and session management
- Background tasks with FastAPI and Celery
5Testing, Logging & Deployment
- pytest with async fixtures and TestClient
- Structured logging with correlation IDs
- Docker and Docker Compose for multi-service apps
- Environment management and secrets handling
6LLM Fundamentals & API Integration
- How large language models work — transformers, attention, tokens
- OpenAI API — chat completions, system prompts, temperature
- Prompt engineering — few-shot, chain-of-thought, output formatting
- Error handling, retries, and cost management for LLM APIs
7Embeddings & Vector Databases
- Text embeddings — what they are, how they work, model selection
- Vector similarity search — cosine, dot product, Euclidean distance
- ChromaDB setup, indexing, and querying
- Pinecone cloud — namespaces, metadata filtering, upserts
8RAG Pipeline Architecture
- RAG architecture — retrieval, augmentation, generation
- Document chunking strategies — fixed-size, recursive, semantic
- Embedding pipeline — extract, chunk, embed, index
- Retrieval quality — re-ranking, hybrid search, metadata filters
9LangChain Orchestration
- LangChain components — chains, prompts, output parsers
- Memory patterns — buffer, summary, and conversation memory
- Sequential and parallel chain composition
- Structured output with function calling and JSON mode
10AI Agents & Tool Use
- Agent architecture — ReAct pattern, plan-and-execute
- Tool design — API calls, database queries, file operations
- Multi-step reasoning and error recovery
- Guardrails, cost limits, and human-in-the-loop approval
11Semantic Search & Hybrid Retrieval
- Hybrid search — combining keyword (BM25) with semantic search
- Re-ranking with cross-encoders for precision
- Multi-modal search — text, images, and structured data
- Search result presentation — snippets, highlights, facets
12AI System Evaluation & Quality
- Evaluation frameworks — RAGAS, faithfulness, relevance metrics
- Automated testing for AI outputs — golden datasets, regression suites
- Human feedback collection and annotation workflows
- A/B testing LLM configurations and prompt variants
13Production AI Backend Patterns
- Streaming responses with Server-Sent Events
- Token usage tracking, billing, and cost alerts
- Caching LLM responses with Redis for repeated queries
- Graceful degradation when AI services are unavailable
14Open-Source LLMs & Fine-Tuning
- Running open-source models with Ollama and vLLM
- Fine-tuning fundamentals — LoRA, QLoRA, and dataset preparation
- Choosing between API-based and self-hosted models
- Model serving, batching, and inference optimization
15Deployment & Monitoring
- Docker Compose for AI service stacks
- Monitoring AI systems — latency, token usage, error rates
- Structured logging for AI request/response audit trails
- CI/CD for AI applications — testing, deployment, rollback
16Capstone Project & Interview Preparation
- End-to-end capstone project execution and code review
- AI backend interview question patterns and system design
- Portfolio presentation and demo preparation
- Resume optimization for AI engineering roles
Hands-On Labs Included
You build these yourself — guided exercises with real tools, not passive demos.
Build a REST API with FastAPI
Docker Lab2 hours
LLM API Integration with OpenAI
Docker Lab2 hours
Vector Search with ChromaDB & Pinecone
Docker Lab2.5 hours
Build a RAG Pipeline from Scratch
Docker Lab3 hours
Build an AI Agent with Tool Use
Docker Lab2.5 hours
AI Evaluation Pipeline with RAGAS
Docker Lab2 hours
Who Is This For?
Career Switchers
Moving from another domain into tech? The structured curriculum and real-world projects bridge the gap between theory and what employers actually look for.
Working Professionals
Already in tech and looking to upskill? Deepen your expertise with production-grade labs and system design patterns used at top companies.
Ideal If You Are:
- Backend developers who want to integrate AI into production systems
- Career switchers with programming experience moving into AI engineering
- Data scientists who want to build production-grade AI services
- Full-stack developers who want to specialize in AI-powered backends
Prerequisites
- Basic Python programming (functions, classes, HTTP concepts)
- Familiarity with REST APIs (endpoints, HTTP methods, JSON)
- A laptop with at least 16 GB RAM for local LLM experiments
- An OpenAI API key (setup guided in Week 6)
Career Support Included
We don't just teach you — we help you land the job.
Mock Interviews
Practice with real-world interview scenarios. Get feedback on technical depth, communication, and problem-solving approach.
Resume Review
One-on-one review sessions to craft a resume that highlights your projects, skills, and achievements the right way.
Portfolio Coaching
Guidance on presenting your course projects as professional portfolio pieces that stand out to hiring managers.
LinkedIn Optimization
Tips and templates to optimize your LinkedIn profile so recruiters find you and reach out.
Learn from Industry Practitioners
Our instructors are working professionals who build production systems daily. They bring real-world experience, battle-tested patterns, and the kind of practical insight that textbooks can't teach.
Course Details
| Format | Live Online |
|---|---|
| Duration | 16 weeks |
| Schedule | 24 sessions |
| Batch Size | Max 15 students |
| Certificate | Yes, on completion |
| Lab Setup | Docker-based (runs on your laptop) |
| Price | Enquire for pricing |
Frequently Asked Questions
Will I get a job after completing this program?
AI backend engineering is one of the highest-demand skill combinations in 2025-2026. Our curriculum covers exactly what companies are hiring for — LLM integration, RAG pipelines, and production AI systems. While we cannot guarantee placement, the skills and projects you build are directly aligned with current job requirements.
Do I need experience with AI or machine learning?
No prior AI experience is required. The first five weeks cover solid backend engineering, and we teach AI concepts from fundamentals. You do need basic Python and REST API knowledge to get the most out of this program.
How much will OpenAI API calls cost during the course?
We design all labs to minimize API costs. Most labs cost under $0.50 in API calls. The total API spend for the entire course is typically under $10. We also cover open-source alternatives that run locally at zero cost.
Is this different from the Generative AI Engineering course?
Yes. The Gen AI course focuses deeply on AI concepts — transformers, fine-tuning, LLMOps. This course focuses on backend engineering with AI integration. Think of it this way: Gen AI makes you an AI specialist, while AI Backend makes you a backend engineer who can build AI-powered features.
Do I need a GPU?
No. All labs run on CPU. For the open-source LLM week, we use quantized models that run on standard laptops. 16 GB RAM is recommended for the local LLM experiments.
What if I miss a live session?
All sessions are recorded and available on the student portal within 24 hours. The instructor and TAs are available on Slack for questions between sessions.
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