Python & Backend Engineering
Production Python, OOP, asynchronous programming, FastAPI, authentication, API development and testing.
PROGRAM
Forward Deployed AI Engineering.
Explore the practical technology areas covered by the program.
LLMs & Generative AI
LLM fundamentals, prompt engineering, embeddings, function calling, structured outputs and model selection.
RAG & Vector Databases
Document chunking, embeddings, vector databases, hybrid search, re-ranking, citations and hallucination reduction.
AI Agents & Agentic Systems
Tool calling, memory, planning, reflection, multi-agent systems, orchestration and human-in-the-loop workflows.
Cloud, DevOps & Deployment
AWS, Azure, Docker, GitHub Actions, Kubernetes fundamentals, monitoring and production deployment.
Enterprise Integrations
Microsoft Graph, SharePoint, Slack, Teams, Jira, Confluence, Salesforce, SAP, ServiceNow and data platforms.
Security & Governance
AI security, prompt injection, guardrails, RBAC, privacy, responsible AI, governance and observability.
Forward Deployed Engineering
Customer discovery, requirements gathering, solution architecture, technical consulting, demos and stakeholder management.
Capstone & Career Preparation
End-to-end AI solution planning, implementation, presentation and practical career preparation.
13 MODULES
Complete Curriculum.
Click any module to expand the topics.
Understanding the FDE role
FDE vs Software Engineer, AI Engineer and Solutions Architect
Customer-first engineering mindset
Business outcome-driven solutioning
Enterprise AI lifecycle and project workflow
Hands-on: Convert business requirements into a solution architecture
Advanced Python programming
Object-Oriented Programming (OOP)
Asynchronous programming
Type hints and code quality
Logging and exception handling
Virtual environments and packaging
Unit testing and production coding standards
Hands-on: Build production-ready Python utilities
REST API fundamentals
FastAPI development
Authentication (OAuth & JWT)
API versioning and documentation
OpenAPI and Swagger
Async API development
Rate limiting and best practices
Hands-on: Develop a secure FastAPI backend
SQL and Advanced SQL
ETL and ELT concepts
Data pipelines
Pandas and PySpark
Delta Lake fundamentals
Databricks basics
Data validation techniques
Hands-on: Build an enterprise data pipeline
AWS fundamentals: IAM, S3, Lambda, API Gateway
Azure fundamentals: Storage, Functions, Key Vault, AI Services
Infrastructure basics
Cloud deployment concepts
Hands-on: Deploy backend services to the cloud
LLM architecture and fundamentals
Prompt engineering
Context windows and tokenization
Embeddings
Function calling
Structured outputs
Model selection and cost optimization
Overview of GPT, Claude, Gemini and Llama
Hands-on: Compare LLMs for enterprise use cases
RAG architecture
Document chunking strategies
Embeddings and vector databases
Metadata filtering
Hybrid search
Re-ranking techniques
Citation generation
Hallucination reduction
LangChain and LlamaIndex
Chroma, Pinecone, PGVector, Azure AI Search
Hands-on: Build an enterprise document assistant
AI agent fundamentals
Tool calling
Memory management
Planning and reasoning
Reflection techniques
Multi-agent systems
Agent orchestration
MCP and A2A protocols
Human-in-the-loop workflows
LangGraph
CrewAI
OpenAI Agents SDK
Amazon Bedrock Agents
Hands-on: Build a multi-agent enterprise assistant
Microsoft Graph API
SharePoint integration
Slack and Microsoft Teams integration
Jira and Confluence integration
Salesforce and SAP connectivity
ServiceNow integration
Databricks and Snowflake integration
Hands-on: Integrate AI with enterprise platforms
Docker and containerization
CI/CD fundamentals
GitHub Actions
Kubernetes basics
Monitoring and logging
Secrets management
Production deployment strategies
Hands-on: Deploy an end-to-end AI application
AI security fundamentals
Prompt injection attacks
AI guardrails
Role-Based Access Control (RBAC)
Data privacy and compliance
Responsible AI principles
AI governance
Observability
Cost management
Hands-on: Secure an enterprise AI solution
Customer discovery workshops
Requirements gathering
Business process mapping
Rapid prototyping
Technical consulting
Solution architecture design
Architecture communication
Stakeholder management
Technical proposal writing
Handling ambiguous requirements
Change management
Customer demos and presentations
Customer success strategies
Workshop: Convert a client problem into an AI solution proposal
Enterprise AI project planning
End-to-end solution design
Production-oriented implementation
Customer problem to AI solution proposal
Presentation and demonstration
CAREER OUTCOMES
Build skills for modern AI roles.
01
Forward Deployed AI Engineer
02
AI Engineer
03
Applied AI Engineer
04
Generative AI Engineer
05
AI Solutions Engineer
06
Enterprise AI Consultant
07
AI Architect
08
AI Infrastructure Engineer