AI Forward Deployed Engineer Bootcamp
5 Months
Launch Your Data Career
Build Production-Ready Enterprise AI Systems
Master the skills to become an AI Forward Deployed Engineer—from software engineering and AWS deployment to RAG, Knowledge Graphs, Multimodal AI, Agentic AI, enterprise integrations, security, and observability
Build real-world enterprise AI solutions using LangGraph, MCP, FastAPI, Docker, AWS, Neo4j, Pinecone, and more with two end-to-end enterprise capstone projects.
Prerequisites: Basic knowledge of Python, SQL, APIs, and Git with a strong problem-solving mindset. No prior experience in GenAI, RAG, Agents, Cloud, or DevOps is required.
Learn. Build. Deploy. Secure. Scale AI for the enterprise.
What You Will Learn
Build the technical depth and enterprise mindset required to take AI systems from prototype to production.
Software & Backend Engineering
Master Python, asynchronous programming, Linux, FastAPI, REST APIs, GraphQL, testing, and production-ready backend development.
Cloud & DevOps
Deploy secure, scalable applications on AWS using VPC, IAM, RDS, Docker, ECS Fargate, and GitHub Actions CI/CD.
Advanced RAG
Build high-accuracy RAG systems using Pinecone, Qdrant, hybrid search, semantic chunking, reranking, and retrieval evaluation.
Knowledge Graphs & Multimodal AI
Work with Neo4j, Cypher, Vision Language Models, ColPali, visual embeddings, and complex enterprise documents including PDFs, tables, and charts.
Agentic & Multi-Agent AI
Build autonomous AI systems with LangGraph, including supervisor and router patterns, parallel execution, memory, human-in-the-loop workflows, and MCP-based tool integration.
Enterprise Integrations
Connect AI systems with Slack, Jira, SQL databases, Oracle, SOAP APIs, and legacy enterprise infrastructure.
AI Security & Governance
Implement OAuth 2.0, SSO, RBAC, prompt-injection protection, PII masking, NeMo Guardrails, and AWS Bedrock Guardrails for secure enterprise AI.
Production AI & Observability
Learn to operate AI systems in production with LLM gateways, rate limiting, retries, evaluation, tracing, latency monitoring, and token-cost observability.
AI Consulting & Delivery
Learn the FDE approach to technical discovery, solution architecture, SOW creation, ROI presentations, UAT, and enterprise deployment handoffs.
Projects You'll Build
In this bootcamp, you’ll gain hands-on experience by building end-to-end enterprise AI systems that simulate real-world client engagements. You’ll work across secure AI integration, RAG, multi-agent orchestration, enterprise systems, governance, and production deployment.
Project 1: OmniGuard — Secure AI Integration
Build a secure enterprise AI integration system designed for real-world corporate environments. You’ll implement Hybrid RAG, secure Text-to-SQL, OAuth 2.0 and RBAC, SQL database integration, NeMo and Presidio guardrails, and a Dockerized FastAPI deployment on the cloud. You’ll also experience the complete FDE consulting lifecycle—from technical discovery and data classification to architecture SOWs, ROI presentations, and UAT runbooks.
Project 2: AuditMesh — Multi-Agent Compliance System
Build a production-oriented multi-agent compliance auditing system using LangGraph and MCP. You’ll design a supervisor-based agent architecture, connect agents securely to Jira through a custom MCP server, implement human-in-the-loop approval workflows, and build token-cost and traceability dashboards. The project also includes defining trust boundaries, SLAs, operational handoffs, and interfaces for human approval.
Course Curriculum
Python & Linux Foundations
- 1Python Core data structures
- 2Memory management fundamentals
- 3Object-oriented programming basics
- 4Exception handling
- 5File I/O operations
- 6Event loop architecture
- 7Coroutines and tasks
- 8Async context managers
- 9Concurrency vs parallelism
- 10ThreadPoolExecutor integration
- 11Navigating the file system
- 12Permission and user management
- 13Process monitoring
- 14Shell scripting basics
- 15Environment variable configurations
Learn from Industry Experts
Why This Course
Enterprise AI Engineering
Go beyond AI prototypes and learn to build production-ready enterprise systems combining software engineering, cloud, AI, security, and business requirements.
Advanced RAG
Build production-grade RAG using semantic chunking, vector databases, hybrid search, reranking, and evaluation with Pinecone and Qdrant.
Agentic AI
Design autonomous and multi-agent systems with LangGraph, including supervisor patterns, parallel execution, memory, human-in-the-loop workflows, and advanced orchestration.
Multimodal AI
Work with complex enterprise documents using Vision Language Models, ColPali, visual embeddings, scanned PDFs, tables, and charts—not just plain text.
Enterprise Integration
Connect modern AI with the systems enterprises actually use—Slack, Jira, SQL databases, Oracle, SOAP APIs, XML, and legacy infrastructure.
AI Security
Security is built into the curriculum. Learn SSO, OAuth, RBAC, prompt-injection defense, PII masking, NeMo Guardrails, and AWS Bedrock Guardrails for enterprise AI.
Production & Observability
Learn what it takes to operate AI systems in production—LLM gateways, retries, rate limits, evaluation, tracing, latency monitoring, and token-cost tracking.
Cloud & Deployment
Take your AI applications from code to production using AWS, Docker, ECS Fargate, networking, IAM, CI/CD, and cloud infrastructure.
Real Enterprise Capstones
Apply everything in two large-scale projects: OmniGuard, a secure enterprise AI integration system, and AuditMesh, a multi-agent compliance platform with MCP and human approval workflows.
Skills You Will Acquire
What Our Students Say
Frequently Asked Questions
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