FORWARD DEPLOYED ENGINEER · THE FUTURE IS HERE
Don't Just Write Code. Build What Comes Next.
Explore the world of Forward Deployed Engineering, where software engineering, applied AI, intelligent agents, and real-world problem solving come together.
POWERED BY CODING MASTERS
WHAT IS FDE?
From Software Engineer to Forward Deployed Engineer
A Forward Deployed Engineer works close to the customer — turning a real business problem into a working, deployed AI solution. Responsibilities vary by company; this is the broader shape of the role.
Software engineering often involves
- Building and maintaining applications
- Implementing technical requirements
- Developing APIs and software features
- Testing and maintaining systems
Forward Deployed Engineering can involve
- Understanding real-world business problems
- Working closely with users and stakeholders
- Building AI-powered applications
- Integrating models, agents, APIs and enterprise systems
- Prototyping, deploying and improving practical solutions
- Connecting engineering work to customer outcomes
WHY FORWARD DEPLOYED ENGINEERING?
Engineering Is Moving Beyond Code.
AI-Powered Applications
LLMs, prompt and model engineering, advanced RAG and multimodal document intelligence.
Intelligent Agents & Workflows
Tool calling, LangGraph, multi-agent systems and MCP with human approval built in.
Real-World Problem Solving
Customer discovery, process analysis, BRDs and technical specs before writing code.
Deployment & Customer Impact
Docker, Kubernetes, CI/CD, UAT, go-live, hypercare and reliability engineering.
AIM HIGHER
Your Next Career Chapter Starts Here.
₹70 LPA
An aspirational benchmark, not a typical or guaranteed outcome. Career results depend on skills, experience, employer, market conditions and individual performance.
Discover the FDE Skill Path →PROGRAM OVERVIEW
Your Roadmap to Forward Deployed Engineering.
Program structure
24 modules across 8 phases — from foundations to forward deployed engineering and production.
Mentor
Led by K SubRaj Sir, Lead Trainer at Coding Masters, with 25+ years of IT experience.
Curriculum
Python, data, backend, frontend, cloud, LLMs, RAG, agents, security, LLMOps, enterprise integration and delivery.
Practical learning
Every module ends with a hands-on mini-project — build something real.
Capstone
OmniGuard — an Enterprise AI Field Service Platform built end to end.
Career readiness
Module 24 covers technical & client presentations, portfolio and interview preparation.
Duration, schedule, fees and batch dates aren't listed in the program document — ask the team on WhatsApp or Instagram.
View the full curriculum ↓COMPLETE CURRICULUM
24 modules. 8 phases. One path.
The full syllabus from the Coding Masters AI Forward Deployed Engineer program, in its original order.
Engineering Foundations
Python Core Foundations
- Python syntax, data structures & control flow
- Functions, modules & packages
- Files, JSON & CSV
- Exception handling & debugging
- Type hints & virtual environments
Advanced Python & OOP
- Classes, objects & inheritance
- Composition, abstraction & encapsulation
- Dataclasses & generators
- Context managers
- Clean code & SOLID principles
Linux for Engineers
- Linux filesystem & permissions
- Processes & resource management
- Shell scripting
- Environment variables
- Logs & troubleshooting
Mini-project — Python System Utility CLI: Build a practical command-line utility using Python, OOP, files, logging and Linux.
Application Engineering
AI Engineering
AI Security & Production
Enterprise Engineering
System Design
Forward Deployed Engineering
Production & Capstone
Want to Know More? Chat with Us →
Ask about duration, schedule, fees and enrolment directly with the Coding Masters team.
PROJECTS & CAPSTONE
Learn by Building Real Solutions.
OmniGuard
Enterprise AI Field Service Platform
One project that brings the whole program together — from customer discovery and BRD/TDD to a secured, deployed and monitored enterprise AI platform.
Business & AI Engineering
- · Customer discovery
- · AS-IS / TO-BE
- · BRD & TDD
- · LLM + RAG
- · Hybrid retrieval & reranking
- · Agentic workflows
Full-Stack & Enterprise
- · React frontend
- · FastAPI backend
- · PostgreSQL / Redis
- · ERP / SAP integration
- · REST / OData
- · Legacy integration
- · MCP
Production & Security
- · Multimodal document intelligence
- · OCR / images / voice
- · SSO / RBAC
- · Tenant isolation
- · Docker / Kubernetes
- · CI/CD
- · LLMOps & evaluation
- · Monitoring & cost control
Mini-projects in every module
MODULE 01
Python System Utility CLI
Build a practical command-line utility using Python, OOP, files, logging and Linux.
MODULE 02
Production-Ready Python Service
Build, test, review and version a small Python application using Git.
MODULE 03
Enterprise Data Analytics API
Design a relational database and build SQL queries and a Python service around it.
MODULE 04
AI Service Backend
Build a FastAPI backend exposing secure APIs for an AI application.
MODULE 05
AI Assistant Web Application
AI interface connected to a backend API.
MODULE 06
Cloud-Ready AI Service
Design and deploy the infrastructure required for a production AI API.
MODULE 07
Containerized AI Application
Dockerize an AI application and create a CI/CD deployment pipeline.
MODULE 08
LLM-Powered AI Assistant
Build an LLM application using structured prompts and controlled model outputs.
MODULE 09
Enterprise Knowledge Copilot
Build a production-style RAG assistant over enterprise documents with citations.
MODULE 10
AI Task Automation Agent
Build an agent that understands a task, selects tools and completes the workflow.
MODULE 11
Multi-Agent Enterprise Assistant
Build a supervisor agent coordinating multiple specialist agents and tools.
MODULE 12
Multimodal Document Assistant
Upload documents/images and extract structured information through an AI assistant.
MODULE 13
Secure AI Copilot
Attack and defend an AI application against common AI security threats.
MODULE 14
AI Evaluation & Monitoring System
Evaluate an AI application and monitor quality, latency, errors and token costs.
MODULE 15
Enterprise AI Integration
Connect an AI application with enterprise APIs and simulate an ERP workflow.
MODULE 16
Secure Enterprise AI Portal
Build an AI application with SSO, roles, permissions and tenant-aware access.
MODULE 17
AI System Architecture
Create HLD, LLD, API flows and architecture decisions for an enterprise AI solution.
MODULE 18
Real-World Automation Discovery
Study a business process and convert the problem into an AI automation opportunity.
MODULE 19
Business Requirement → AI Solution
Create BRD + TDD + AI technical specifications for an enterprise AI project.
MODULE 20
Client Delivery Simulation
Run a complete sprint from requirements to demo and stakeholder feedback.
MODULE 21
AI Production Go-Live
Execute UAT, defect tracking, go-live planning and post-launch hypercare.
MODULE 22
AI Production Incident Lab
Diagnose and recover an AI system suffering from latency, errors or data/retrieval failures.
MODULE 24
Final Deliverable
FDE portfolio + enterprise technical presentation.
K SubRaj Sir
Lead Trainer, Coding Masters
TRANSFORMING PROFESSIONALS INTO AI INNOVATORS
MENTOR
Learn from Experience.
With over 25+ years of IT experience and an M.Sc. in Computer Science from Manipal University, K SubRaj Sir is a leading trainer in Data Science, Prompt Engineering, LLMs, Generative AI, Agentic AI, and Autonomous Testing (AI). He emphasizes hands-on, industry-oriented learning that bridges the gap between academic concepts and real-world applications.
Certifications & credentials
- Microsoft Certified Data Scientist & Copilot Engineer
- Google Certified Gen-AI Engineer
- Certified Pythonista Programmer
- Certified AI in Testing (ISTQB)
- Certified in GenAI & Agentic AI Engineering (Google, Microsoft, ITG)
Specialties & expertise
- Data Science, AI & Generative AI
- Prompt Engineering & Agentic AI
- Python Programming & AI Testing
- Corporate & Academic Training (250+ hours courses)
- Mentoring professionals for career growth in AI
TECHNOLOGY ECOSYSTEM
Connect AI to the Real World.
Programming & Apps
Data
GenAI & LLMs
Agentic Workflows
Cloud & Deployment
Enterprise & Security
WHO IS THIS FOR?
Built for engineers ready to go further.
Software engineers
Extend your coding skills into AI systems, agents and customer-facing delivery.
Backend developers
Build on APIs and data skills with FastAPI, RAG, tool calling and enterprise integration.
Full-stack developers
Ship complete AI apps — React interfaces, streaming chat UX and production backends.
Platform engineers
Apply cloud, Kubernetes and CI/CD to LLMOps, observability and reliable AI.
Experienced developers exploring applied AI
A structured path from foundations to an enterprise capstone and portfolio.
Prerequisites and eligibility aren't specified in the program document — please confirm with the team.
LEARNING OUTCOMES
Build Skills That Go Beyond Coding.
Build AI applications with LLMs and structured prompts
Design production-grade RAG with citations and hybrid retrieval
Engineer safe agents, multi-agent workflows and MCP integrations
Secure AI systems with guardrails and red teaming
Evaluate and monitor AI quality, latency and cost
Integrate AI with SAP, Salesforce, ServiceNow and legacy systems
Design AI systems with HLD, LLD and architecture decisions
Run discovery, write BRDs/TDDs and deliver to stakeholders
Deploy with Docker, Kubernetes, CI/CD and handle go-live
THE LEARNING JOURNEY
One path, five big moves.
Learn core skills
Phases I–II
Engineer AI systems
Phases III–IV
Work with the enterprise
Phases V–VI
Deploy forward
Phase VII
Capstone & portfolio
Phase VIII
Career pathways to explore
Role titles vary by employer. The program builds skills relevant to these paths; it does not guarantee a specific role.
FAQ
Questions, answered.
An engineer who works close to customers to understand real business problems and build, integrate and deploy practical AI solutions — combining software engineering, applied AI and delivery skills.
The future is forward deployed.
Talk to the Coding Masters team about the program, curriculum and enrolment.