NEWFDE
Certified Forward Deployed Engineer (CFDE) Master Program: Cloud, GenAI, and Enterprise Integration
A comprehensive program that equips engineers to translate ambiguous customer needs into production-ready, enterprise-grade AI and cloud solutions through rapid, customer-centric engineering and multi-agent system design.
4.6(0 reviews)0 students7 lessonsProfessional Certificate
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Course Content
Module 1: Foundations of Forward-Deployed Engineering
4 LessonsThe FDE Role: Comparison between FDEs, Software Engineers, Machine Learning Engineers, and Solutions Architects.
The FDE Mindset: Empathy over assumptions; managing the balance between rapid prototyping and production rigor.
Problem Structuring: Business-to-technical translation frameworks and identifying signal vs. noise in client requirements.
Feasibility Analysis: Understanding constraints, managing trade-offs, and first-principles problem decomposition.
Module 2: Production-Ready Software & Backend Development
4 LessonsPython for the Enterprise: Writing clean, modular, and testable code; separation of concerns.
Quality & Dependencies: Virtual environments, dependency management, linting, formatting, and unit testing (pytest).
API Design: REST fundamentals, designing JSON-based endpoints with clear contracts, and asynchronous routing using FastAPI.
Security & Stability: Authentication basics (API keys, JWT), robust error handling, and automated API documentation (Swagger/OpenAPI).
Module 3: Data Engineering & Pipeline Thinking
4 LessonsData Movement: How data flows through modern enterprise systems; batch vs. streaming design patterns.
Data Processing: ETL (Extract, Transform, Load) vs. ELT principles and their practical trade-offs.
Storage Strategies: Relational databases, NoSQL, and an introduction to Vector Databases for AI workloads.
Connecting Systems: Linking backend logic with persistent storage and ensuring data observability.
Module 4: Generative AI, LLMs, and RAG
4 LessonsPrompt Engineering: Structuring patterns (zero-shot, few-shot, chain-of-thought) and JSON-mode structured outputs.
LLM Workflows: Chaining, routing, managing fallbacks, and handling non-determinism in AI outputs.
Retrieval-Augmented Generation (RAG): Building custom RAG pipelines using embeddings and Vector DBs.
Optimization: Managing cost, latency, and token limits in production environments; LLM evaluation metrics.
Module 5: Agentic AI Paradigms and Frameworks
4 LessonsAgentic vs. Standard AI: Understanding the shift from static LLM calls to autonomous AI agents.
Multi-Agent Orchestration: Architecting systems where multiple specialized AI agents collaborate to solve complex tasks.
Frameworks in Practice: Building and debugging agent workflows using LangChain and LangGraph.
Tool Calling: Enabling AI agents to execute code, query databases, and interact with external enterprise APIs.
Module 6: Cloud-Native Architecture & DevOps Automation
4 LessonsCloud Fundamentals: Refresher on core AWS and GCP services utilized in client deployments.
Containerization: Fundamentals of packaging applications using Docker and orchestrating them with Kubernetes.
CI/CD Implementation: Automating testing and deployment using GitHub Actions and Jenkins.
AI Deployment: Deploying LLMs and Agentic AI applications using managed cloud services (e.g., Amazon Bedrock) vs. local/on-premise deployments.
Module 7: Security, Observability, and Compliance
3 LessonsEnterprise Integration: Navigating complex client network topologies, firewalls, and security requirements.
System Observability: Implementing logging, metrics, and tracing to ensure deployed solutions are monitorable.
Continuous Improvement: Capturing real-world production feedback and establishing improvement loops for deployed models and applications.
Fee Structure
Instructor-Led₹1,10,000
- Live sessions
- Personal mentor
- Doubt-clearing sessions
- Job assistance
- Verified certificate
Batch Training₹90,000
- Scheduled batch timings
- Group learning
- Peer collaboration
- Certificate on completion