COMPLETE SYLLABUS
AI Engineer Program syllabus.
From Python fundamentals to production-grade agentic AI systems: 14 modules, 71 topics and 15 production-grade projects, taught online and offline in Bhubaneswar.
- 14modules
- 71topics
- 15production-grade projects
- 24–30weeks
How you learn: online or offline batches · 3 classes a week on alternate days · 1.5–2 hour sessions · pre-read material before every class · a graded assignment in every class · weekly tests and module tests · your own laptop for code and projects · a shared GitHub workspace to push, merge and collaborate.
What you will learn, module by module
MODULE 01
Python Foundations
- Setup and interpreter basics
- Control flow and functions
- Core data structures
- Modules
- File I/O, JSON and error handling
- Virtual environments and packages
MODULE 02
Data Science with NumPy & Pandas
- NumPy arrays and vectorization
- DataFrames
- Data cleaning and joins
- Exploratory data analysis and visualization with Pandas and Matplotlib
MODULE 03
Machine Learning with Scikit-Learn
- Frame a machine learning problem
- Regression and classification
- Model evaluation
- Pipelines and preprocessing
- Cross-validation and hyperparameter tuning
- Model serving with FastAPI
- End-to-end deployment project
MODULE 04
Deep Learning with PyTorch
- Deep learning mental models
- Tensors and autograd
- Neural networks
- Training and regularization
- Production-grade deep learning project
MODULE 05
NLP with Hugging Face
- Working with text inputs
- Embeddings
- Transformers and attention
- Running inference
- Production-grade NLP project
MODULE 06
LLMs — OpenAI, Anthropic & Gemini
- Build an LLM mental model
- Work with LLM APIs
- Prompt engineering
- Structured outputs
- Model comparison
- Production-grade LLM projects
MODULE 07
Retrieval-Augmented Generation (RAG)
- RAG architecture
- Chunking and embeddings
- FAISS, Milvus, PgVector and MongoDB
- LangChain and LangGraph
- LangSmith evaluation
- OWASP LLM Top 10 security
MODULE 08
AI Agents
- The agent loop
- Tool calling
- State and routing in LangGraph
- Memory and human-in-the-loop
- Multi-agent systems
MODULE 09
Production Engineering, DevOps & AI Security
- Production APIs with FastAPI and OpenAPI
- Docker
- AWS
- CI/CD with GitHub Actions
- LLMOps with OpenTelemetry and LangSmith
- OWASP Top 10 AI security
- Enterprise architecture
MODULE 10
Advanced Protocols — MCP, A2A & Agentic Systems
- Model Context Protocol (MCP)
- Agent-to-Agent (A2A)
- Agentic RAG
- AI harnesses
- Production-grade projects around MCP, A2A and AI harnesses
MODULE 11
Interview Prep
- How to speak and behave in an interview
- How to code and explain in an interview
- What questions to ask during an interview
- What to discuss and how to approach the HR round
- 5 mock interviews
MODULE 12
Job Readiness
- Support for LinkedIn profile, summary, skills and projects upgrade
- Support for 10 LinkedIn posts
- Recommendations
- Resume writing support with keywords so that it passes ATS
- Business etiquette: writing emails, talking to the technical team, HR and leadership
MODULE 13
AI / Software Engineering
- AI / software development lifecycle
- Agile methodology and Scrum meetings
- 12-factor application development
- Best practices, standards and guardrails
- Git essentials and CI/CD pipeline
- AWS deployment, monitoring, alerts and observability
- Debugging
- Security essentials including OWASP Top 10 and OWASP AI
- JIRA tickets
MODULE 14
Final Capstone Projects
- 15 production-grade capstone projects spanning deployment, security, observability and delivery
The topics in this syllabus grow with industry needs and standards. Questions? Write to support@logicraft.ai or call 98277 10731.