Translate the need
Turn user and business problems into clear technical requirements and a scope you can deliver.
APPLIED AI ENGINEERING
AI Forward Deployed Engineer
Build. Integrate. Deploy.
Put AI to work where it matters. Turn customer needs into working software, connect LLMs, retrieval and agents to real systems, and ship AI features people rely on.
- LLMs, RAG & agents
- Production AI workflows
- Customer-facing delivery
ENQUIRE ABOUT THE COURSE
Your next chapter
Your next chapter
starts here.
Tell us a little about yourself.
Complete your syllabus request in the open window.
YOUR STARTING POINTBasic programming
YOUR ROADMAP76 learning modules
COURSE DURATION6–8 Months
YOUR LEARNING MODEClassroom or live
THE ROLE BETWEEN AI AND THE CUSTOMER
Understand the need.
Deploy the solution.
Forward deployed engineers sit where AI meets the real world. Learn to listen to a problem, design an AI-powered answer, and own it all the way into production.
Know how to code? Ready to build with AI.
For students and working professionals comfortable with programming basics who want to build, integrate and deploy AI-powered applications.
Integrate AI into systems
Build backend APIs, connect databases, and wire LLMs, retrieval and agents into applications.
Ship and iterate
Evaluate, deploy and monitor AI workflows, then improve them based on real feedback.
YOUR LEARNING ROADMAP
One concept leads
to the next breakthrough.
Move from backend foundations to LLM integration, retrieval, agents and production delivery. Each module adds a layer you will use when deploying AI for real users.
THE FORWARD DEPLOYED MINDSET
The model is the start. Deployment is the job.
Calling a model is easy. Making it reliable inside a customer’s systems takes engineering: clear requirements, grounded data, safe tool use, evaluation and monitoring. Learn to deliver AI that keeps working after the demo.
- Start from the problem
- Measure, don’t guess
- Own it in production
Your complete learning roadmap.
Download syllabus
Keep the module breakdown, practice assignments and learning outcomes.
Suggested curriculum. Confirm the current batch syllabus, module allocations and total duration with an advisor.
Track 1 · Modules 01–21 Backend Engineering
01 JavaScript Programming Fundamentals
- Introduction to JavaScript
- Variables
- Data Types
- Operators
- Conditional Statements
- Loops
Put it into practice
Write small programs using variables, conditions and loops.
What you’ll take away
Write clear, correct JavaScript fundamentals.
02 JavaScript Functions and Core Concepts
- Functions
- Parameters
- Return Values
- Scope
- Closures
- Arrays
- Objects
- Common Array Methods
Put it into practice
Solve data problems using functions and array methods.
What you’ll take away
Structure logic into reusable functions.
03 Modern JavaScript and ES6+ Features
- Destructuring
- Spread Operators
- Modules
- Classes
- Template Literals
- Optional Chaining
Put it into practice
Refactor older code into modern ES6+ modules.
What you’ll take away
Write concise, modern JavaScript.
04 Asynchronous JavaScript and Error Handling
- Callbacks
- Promises
- Async/Await
- Asynchronous Programming
- Try/Catch
- Custom Errors
- Validation
Put it into practice
Build an async data loader with proper error handling.
What you’ll take away
Handle async flows and failures safely.
05 Introduction to Node.js
- Node.js Runtime
- Server-Side JavaScript
- Event Loop
- Non-Blocking Execution
- Asynchronous Operations
Put it into practice
Build a small command-line tool with Node.js.
What you’ll take away
Explain how Node.js runs non-blocking code.
06 Node.js Core Modules
- File System
- Path Module
- Events
- Streams
- Buffers
- HTTP Module
Put it into practice
Build a file-processing script and a basic HTTP server.
What you’ll take away
Use Node.js core modules confidently.
07 Express.js Fundamentals and Routing
- Introduction to Express.js
- Backend Application Development
- Routing
- Controllers
- Business Logic
Put it into practice
Build an Express app with routes and controllers.
What you’ll take away
Structure backend applications cleanly.
08 Express.js Middleware and API Development
- Authentication Middleware
- Validation Middleware
- Logging
- Error Handling
- REST API Development
Put it into practice
Add validation, logging and error middleware to an API.
What you’ll take away
Build robust REST APIs with middleware.
09 REST API Design and Documentation
- REST API Architecture
- API Design
- Swagger
- OpenAPI
- API Testing
- Documentation
Put it into practice
Design, test and document an API with OpenAPI.
What you’ll take away
Design APIs others can understand and use.
10 Introduction to Databases and Data Modelling
- Relational and Non-Relational Databases
- Database Design
- Data Modelling
- Database Relationships
Put it into practice
Model the data for a real application.
What you’ll take away
Choose and design suitable data models.
11 MongoDB and Mongoose
- MongoDB
- Mongoose
- Schemas
- Models
- Validations
- Database Relationships
Put it into practice
Build a MongoDB-backed API with validated schemas.
What you’ll take away
Work with document databases in Node.js.
12 PostgreSQL and SQL
- PostgreSQL
- SQL Queries
- Tables
- Joins
- Indexing
- Relational Data
Put it into practice
Write queries and joins on a relational dataset.
What you’ll take away
Query and index relational data efficiently.
13 User Authentication and Session Management
- User Registration
- Login
- Logout
- JWT Authentication
- Refresh Tokens
- Session Management
Put it into practice
Add JWT login with refresh tokens to an API.
What you’ll take away
Implement secure user authentication.
14 OAuth and Role-Based Authorization
- OAuth Authentication
- Google Login
- GitHub Login
- Role-Based and Permission-Based Access Control
Put it into practice
Add social login and role-based access to an app.
What you’ll take away
Control who can do what in an application.
15 Backend Security and Application Performance
- Input Validation
- Hashing
- Rate Limiting
- Secure Headers
- Redis
- Caching
Put it into practice
Harden an API and add Redis caching.
What you’ll take away
Build secure, fast backends.
16 Background Jobs and Asynchronous Processing
- Background Jobs
- Queues
- Asynchronous Emails
- Notifications
- Heavy Task Processing
Put it into practice
Move email and heavy tasks into a job queue.
What you’ll take away
Keep applications responsive with background work.
17 Real-Time Application Development
- WebSockets
- Real-Time Communication
- Chat Applications
- Notifications
- Live Updates
Put it into practice
Build a real-time chat with live notifications.
What you’ll take away
Add real-time features with WebSockets.
18 Backend Testing and Debugging
- Unit Testing
- Integration Testing
- API Testing
- Debugging Backend Applications
Put it into practice
Write unit and integration tests for an API.
What you’ll take away
Test and debug backend code with confidence.
19 Clean Architecture and Microservices
- Clean Architecture
- Design Patterns
- Maintainable Code
- Microservices
- Inter-Service Communication
Put it into practice
Split an application into communicating services.
What you’ll take away
Design maintainable, modular backends.
20 High-Level and Low-Level System Design
- Scalable System Architecture
- API Design
- Database Design
- Application Workflows
- Reusable Classes
- Modules
- Components
Put it into practice
Design a scalable system from requirements to classes.
What you’ll take away
Reason about architecture at every level.
21 Backend Application Deployment
- Docker
- Containerization
- Deploying Backend Applications on Cloud Platforms
Put it into practice
Containerise and deploy a backend to the cloud.
What you’ll take away
Ship backend applications to production.
Track 2 · Modules 22–46 Generative AI
22 Introduction to Artificial Intelligence
- Overview of Artificial Intelligence
- Generative AI
- Discriminative Models
- Generative Models
Put it into practice
Compare generative and discriminative models on examples.
What you’ll take away
Explain the landscape of modern AI.
23 Mathematics for Artificial Intelligence
- Probability
- Statistics
- Linear Algebra
- Mathematical Concepts Used in AI
Put it into practice
Solve probability and linear algebra exercises in Python.
What you’ll take away
Understand the maths behind AI models.
24 Gradient Descent and Optimization
- Introduction to Gradient Descent
- Optimization Techniques
- Model Optimization
Put it into practice
Implement gradient descent from scratch.
What you’ll take away
Explain how models learn through optimisation.
25 Neural Networks Fundamentals
- Introduction to Neural Networks
- Working of Neural Networks
- Feedforward Neural Networks
Put it into practice
Build and train a feedforward neural network.
What you’ll take away
Understand how neural networks learn.
26 Neural Network Architectures
- Recurrent Neural Networks (RNNs)
- Convolutional Neural Networks (CNNs)
- Neural Network Architectures
Put it into practice
Train a CNN on images and an RNN on sequences.
What you’ll take away
Choose architectures for different data types.
27 Generative Adversarial Networks (GANs)
- Introduction to GANs
- Generative Adversarial Networks
- Their Working
Put it into practice
Train a simple GAN to generate images.
What you’ll take away
Explain how adversarial training works.
28 Advanced GAN Architectures
- Deep Convolutional GANs (DCGAN)
- StyleGAN
- Their Architectures
Put it into practice
Generate higher-quality images with a DCGAN.
What you’ll take away
Compare advanced GAN architectures.
29 Variational Autoencoders (VAEs)
- Introduction to Variational Autoencoders
- VAE Architecture
- Probabilistic Data Generation
Put it into practice
Build a VAE that generates new samples.
What you’ll take away
Understand probabilistic generative models.
30 Diffusion Models
- Introduction to Diffusion Models
- Denoising Diffusion Probabilistic Models (DDPMs)
- Their Applications
Put it into practice
Run and explore a denoising diffusion model.
What you’ll take away
Explain how diffusion models generate data.
31 Introduction to Transformers
- Transformer Architecture
- Attention Mechanism
- Self-Attention
Put it into practice
Implement self-attention step by step.
What you’ll take away
Understand the core of transformer models.
32 Advanced Transformer Concepts
- Positional Encoding
- Encoder-Decoder Architecture
- Transformer-Based Language Models
Put it into practice
Build a small encoder-decoder transformer.
What you’ll take away
Explain how full transformer models work.
33 Introduction to Large Language Models
- Large Language Models
- GPT
- BERT
- Popular Language Model Architectures
Put it into practice
Compare GPT and BERT on common NLP tasks.
What you’ll take away
Choose the right LLM architecture for a task.
34 LLM Pretraining Techniques
- Pretraining Approaches
- Masked Language Modeling (MLM)
- Causal Language Modeling (CLM)
Put it into practice
Pretrain a tiny language model on sample text.
What you’ll take away
Explain how LLMs are pretrained.
35 LLM Fine-Tuning
- Fine-Tuning Approaches
- Task-Specific Fine-Tuning
- Text Summarization
- Question Answering
Put it into practice
Fine-tune a model for summarisation or Q&A.
What you’ll take away
Adapt pretrained models to specific tasks.
36 Introduction to Retrieval-Augmented Generation
- RAG Fundamentals
- RAG Architecture
- Retrieval Pipeline
- Integration with Large Language Models
Put it into practice
Build a basic RAG pipeline over documents.
What you’ll take away
Ground LLM answers in external data.
37 Information Retrieval Systems
- BM25
- FAISS
- Elasticsearch
- Document Retrieval Systems
Put it into practice
Compare BM25 and vector search on one dataset.
What you’ll take away
Choose the right retrieval approach.
38 Vector Databases and RAG Applications
- Introduction to Vector Databases
- Integration of Retrieval Systems with LLMs
- Practical Applications of RAG
Put it into practice
Build a RAG app backed by a vector database.
What you’ll take away
Build production-style retrieval applications.
39 Introduction to Multimodal AI
- Multimodal Models
- Vision and Text Models
- CLIP
- DALL-E
Put it into practice
Search images with text using CLIP.
What you’ll take away
Work with models that combine text and images.
40 Introduction to LangChain
- LangChain Fundamentals
- Installation
- Setup
- Prompts
- Memory
- Chains
- Agents
Put it into practice
Build a LangChain app with prompts, memory and chains.
What you’ll take away
Compose LLM applications with LangChain.
41 Building RAG Applications with LangChain
- LangChain RAG Pipelines
- Retrieval Integration
- Generating Responses Using External Knowledge Sources
Put it into practice
Build a LangChain RAG app over your own documents.
What you’ll take away
Build RAG applications quickly with LangChain.
42 Knowledge-Aware Chatbot Development
- Building Chatbots Using LLMs
- External Data Sources
- Custom Knowledge Bases
Put it into practice
Build a chatbot that answers from a custom knowledge base.
What you’ll take away
Ship chatbots grounded in real data.
43 Domain-Specific LLM Fine-Tuning
Fine-Tuning Open-Source Language Models for Specific Domains such as Finance and Healthcare
Put it into practice
Fine-tune an open-source model on domain data.
What you’ll take away
Specialise LLMs for industry domains.
44 Text-to-Image Generation
Building Text-to-Image Generation Systems Using DALL-E and Stable Diffusion
Put it into practice
Build a text-to-image generator.
What you’ll take away
Integrate image generation into applications.
45 Prompt Engineering and Model Optimization
- Prompt Engineering Techniques
- Prompt Optimization
- Improving Model Outputs
Put it into practice
Iterate on prompts and measure output quality.
What you’ll take away
Get reliable, high-quality model outputs.
46 Introduction to AI Agents
- AI Agents
- Role of AI Agents
- Their Practical Applications Across Different Industries
Put it into practice
Build a simple agent that uses a tool.
What you’ll take away
Explain where AI agents add value.
Track 3 · Modules 47–70 DevOps & Cloud Engineering
47 Linux Fundamentals
- Introduction to Linux
- Linux File System
- Essential Commands
- File Permissions
- Process Management
Put it into practice
Manage files, permissions and processes on a Linux server.
What you’ll take away
Work confidently on the Linux command line.
48 Shell Scripting and Automation
- Bash Scripting
- Variables
- Conditions
- Loops
- Functions
- Automation Scripts
Put it into practice
Automate a routine server task with a Bash script.
What you’ll take away
Automate repetitive work with shell scripts.
49 Git Fundamentals
- Git Installation
- Repository Setup
- Git Add, Commit, Status and Log
- Pull Requests & Git Workflows
Put it into practice
Track a project's history with Git.
What you’ll take away
Manage code changes with Git.
50 GitHub and Collaborative Development
- GitHub
- Clone
- Push
- Pull
- Branching
- Pull Requests
- Team Collaboration
Put it into practice
Collaborate on a shared repository with pull requests.
What you’ll take away
Work in teams using GitHub workflows.
51 Introduction to Cloud Computing and AWS
- Cloud Computing Concepts
- AWS Account Setup
- IAM Users
- Roles
- Permissions
- Multi-Factor Authentication
Put it into practice
Set up a secure AWS account with IAM and MFA.
What you’ll take away
Understand cloud fundamentals and access control.
52 AWS EC2 and S3
- EC2 Instances
- Security Groups
- SSH Access
- S3 Buckets
- Static Website Hosting
Put it into practice
Launch an EC2 instance and host a site on S3.
What you’ll take away
Use core AWS compute and storage services.
53 AWS Deployment Fundamentals
- Creating IAM Users
- Deploying Web Servers on EC2
- Hosting Websites Using Amazon S3
Put it into practice
Deploy a web server on EC2 end to end.
What you’ll take away
Deploy applications on AWS.
54 Introduction to Docker
- Containers
- Virtual Machines
- Docker Installation
- Docker Images
- Containers
Put it into practice
Run applications in Docker containers.
What you’ll take away
Explain containers and when to use them.
55 Dockerfile and Container Management
- Dockerfile
- Creating Custom Images
- Image Tagging
- Container Management
- Volumes
- Bind Mounts
Put it into practice
Write a Dockerfile for your own application.
What you’ll take away
Build and manage custom container images.
56 Docker Networking and Docker Compose
- Docker Networking
- Port Mapping
- Docker Compose
- Services
- Dependencies
- Multi-Container Applications
Put it into practice
Run a multi-container app with Docker Compose.
What you’ll take away
Orchestrate multi-service applications locally.
57 Introduction to CI/CD and Jenkins
- Continuous Integration
- Continuous Deployment
- Jenkins Installation
- Dashboard
- Plugins
- Jobs
- Build Triggers
Put it into practice
Set up a Jenkins job that builds on every push.
What you’ll take away
Explain and set up CI/CD basics.
58 Jenkins Pipelines and Automation
- GitHub Integration
- Jenkinsfile
- Declarative Pipelines
- Pipeline Stages
- Steps
Put it into practice
Write a declarative Jenkins pipeline.
What you’ll take away
Automate builds with pipelines as code.
59 Docker Integration with Jenkins
- Building and Pushing Docker Images
- Managing Credentials
- Automating CI/CD Pipelines
Put it into practice
Build and push Docker images from Jenkins.
What you’ll take away
Automate container delivery pipelines.
60 Introduction to Kubernetes
- Kubernetes Architecture
- Kubectl
- Minikube
- Pods
- Deployments
Put it into practice
Run an application on a local Kubernetes cluster.
What you’ll take away
Understand Kubernetes building blocks.
61 Kubernetes Deployment and Scaling
- Deployments
- Scaling Applications
- Rolling Updates
Put it into practice
Scale an app and roll out an update with zero downtime.
What you’ll take away
Scale and update applications safely.
62 Kubernetes Services and Networking
- ClusterIP
- NodePort
- LoadBalancer
- Service Discovery
Put it into practice
Expose an application with Kubernetes services.
What you’ll take away
Connect and expose services in a cluster.
63 Kubernetes Configuration and Storage
- ConfigMaps
- Secrets
- Environment Variables
- Persistent Volumes
- Persistent Volume Claims
Put it into practice
Configure an app with ConfigMaps, Secrets and volumes.
What you’ll take away
Manage configuration and persistent data.
64 Kubernetes Monitoring and Operations
- Kubernetes Logs
- Metrics
- Storage
- Application Configuration
Put it into practice
Inspect logs and metrics to diagnose a failing pod.
What you’ll take away
Operate and troubleshoot Kubernetes workloads.
65 AWS Elastic Container Registry (ECR)
- Introduction to AWS ECR
- Creating ECR Repositories
- Pushing Docker Images
Put it into practice
Push your images to a private ECR repository.
What you’ll take away
Manage container images on AWS.
66 AWS Elastic Kubernetes Service (EKS)
- Introduction to Amazon EKS
- Creating EKS Clusters
- Connecting with Kubectl
- Deploying Containerized Applications
Put it into practice
Deploy a containerised app to an EKS cluster.
What you’ll take away
Run Kubernetes workloads on AWS.
67 End-to-End CI/CD Integration
- Integrating Jenkins, Docker, GitHub and Kubernetes
- GitHub Webhooks
- Automated Builds
- Deployments
Put it into practice
Build a pipeline from GitHub push to Kubernetes deploy.
What you’ll take away
Connect the full delivery toolchain.
68 Continuous Delivery with Kubernetes
- Implementing Continuous Delivery
- Automated Deployment Pipelines
- Kubernetes Deployment Automation
Put it into practice
Automate deployments to Kubernetes on every release.
What you’ll take away
Deliver changes continuously and safely.
69 Monitoring with AWS CloudWatch
- AWS CloudWatch
- Kubernetes Logs
- Application Metrics
- Monitoring Deployed Applications
Put it into practice
Set up CloudWatch dashboards and alarms.
What you’ll take away
Monitor deployed applications on AWS.
70 Prometheus and Grafana
- Introduction to Prometheus
- Grafana
- Application Monitoring
- Troubleshooting
Put it into practice
Build a Grafana dashboard from Prometheus metrics.
What you’ll take away
Monitor and troubleshoot applications.
Track 4 · Modules 71–76 Industry Projects
71 Enterprise Knowledge-Aware Chatbot
- Building an AI Chatbot Using LLMs
- Company Documents
- PDFs
- Custom Knowledge Bases
- Retrieval-Augmented Generation
- Source-Backed Responses
Put it into practice
Build a chatbot that answers from company documents with sources.
What you’ll take away
Ship an enterprise-grade RAG assistant.
72 Domain-Specific LLM Fine-Tuning
Fine-Tuning an Open-Source LLM for Domains such as Finance, Healthcare, Education or Recruitment
Put it into practice
Fine-tune and evaluate a model for one industry domain.
What you’ll take away
Deliver a specialised LLM for real users.
73 AI Image Generation Platform
Building a Text-to-Image Generation Application Using DALL-E or Stable Diffusion
Put it into practice
Build and deploy a text-to-image application.
What you’ll take away
Ship a generative media product.
74 AI-Powered Customer Support Agent
- Building a Customer Support Agent
- Answering Customer Queries
- Knowledge Base Retrieval
- Performing Actions Using External Tools
Put it into practice
Build an agent that resolves queries and takes actions.
What you’ll take away
Deploy agents that act through tools.
75 Prompt Engineering & Evaluation Studio
- Building a Platform for Prompt Testing
- Comparing Model Responses
- Prompt Optimization
- Accuracy
- Relevance
- Structured Outputs
Put it into practice
Build a tool to test and compare prompts and models.
What you’ll take away
Evaluate LLM outputs systematically.
76 AI Workflow Automation Orchestration Assistant
- Building an AI Agent Using APIs
- Databases
- Business Tools
- Multi-Step Workflow Orchestration Automations
Put it into practice
Build an agent that automates a multi-step business workflow.
What you’ll take away
Orchestrate agents across real business systems.
Takeaway from the course
- Python
- SQL & PostgreSQL
- REST APIs & FastAPI
- React & TypeScript
- Git & GitHub
- LLMs
- Prompt & Context Engineering
- Structured Outputs
- Embeddings & Vector Databases
- RAG
- AI Agents & Tool Calling
- Multi-Agent Workflows
- MCP
- Enterprise Integrations (CRM, ERP, SaaS APIs)
- Data Pipelines & ETL
- Agent Evaluation
- Guardrails & Human-in-the-Loop
- Observability & Tracing
- Docker & Cloud Deployment
- AI Security & Data Privacy
- Cost & Latency Optimization
- AI-Assisted Coding
- Requirements & Scoping
- Customer Discovery & Demos
- Projects
PUT THE IDEAS TO WORK
Make the concepts
something you can show.
Go beyond notebooks and demos. These project ideas combine APIs, models, data and deployment into systems you can build, test and walk a customer through.
Retrieve → Ground → AnswerCited ✓
PROJECT IDEA · GROUNDED ANSWERS
Document Q&A assistant
Index a company’s documents, retrieve the right passages, and answer questions with citations users can verify.
Agent · 3 toolsAwaiting approval
PROJECT IDEA · AUTOMATE THE WORKFLOW
Workflow automation agent
Give an agent tools for a real business process, add guardrails, and keep a human in the loop for key decisions.
PDF → Fields → DatabaseValidated ✓
PROJECT IDEA · STRUCTURED OUTPUT
Document data extractor
Turn messy documents into structured, validated records and load them into a database through an API.
Eval set · regression checkShip v2 ✓
PROJECT IDEA · MEASURE BEFORE YOU SHIP
LLM evaluation harness
Build a test set, score model outputs automatically, and catch regressions before a change reaches users.
Docker · Tracing · AlertsHealthy ✓
PROJECT IDEA · CUSTOMER CAPSTONE
End-to-end client deployment
Take a real-world brief from discovery to a deployed, monitored AI solution, then present it to stakeholders.
Illustrative project previews. Your mentor will confirm project scope and review criteria for your batch.
HOW THE LEARNING COMES TOGETHER
Understand it.
Then make it your own.
A learning rhythm that connects the concept, the code and your reasoning. Here’s how you can turn each new topic into a skill.
Understand the system
See how models, data and services fit together. Understand when an approach works and where it fails.
Build and integrate
Ship working features through assignments. Debug real failures, test edge cases and measure quality.
Present it like an FDE
Explain your design to a non-technical stakeholder, justify trade-offs in cost, latency and quality, and act on feedback.
Ask your advisor about assignment reviews, doubt support, recordings and mock interview inclusions for your chosen batch.
FIND YOUR LEARNING ENVIRONMENT
Same ambition.
Your way to learn.
Choose the environment that suits your routine. Our advisors can help you compare current batches, schedules and support.
Learn in a classroom.
Bring your questions to class, learn alongside peers, and build a routine around in-person sessions.
Explore Noida & NSP centres Find a classroom batchJoin from where you are.
Attend live sessions remotely. Find a batch whose timing and learning support work for your day.
Live sessions · learn remotely Explore live batches
UP TO100%SCHOLARSHIP
CAST · YOUR OPPORTUNITY TO LEARN
Apply for CAST
Your ambition. A scholarship to match.
Apply for CAST — the Coding Blocks Aptitude and Scholarship Test — and earn a scholarship of up to 100% on course fees.
Available for classroom and Live interactive courses, including Sampoorna. Awards are subject to the test’s eligibility and award terms.
YOUR CENTRE. YOUR SCHEDULE.
Your centre. Your batch.
Choose NSP or Noida, then find the weekday or weekend schedule that fits you.
AI FDE at NSP, Delhi
No upcoming batches
See the centre
No batches are currently listed for this selection. Contact our team about the next intake.
Talk to an advisor
Your selected batch
A LITTLE CLARITY
Good questions.
Clear answers.
What is a forward deployed engineer?
A forward deployed engineer works closely with customers to understand their problems, then builds and deploys software, increasingly AI-powered, inside their real systems. The role combines engineering, product thinking and clear communication.
Do I need prior AI or machine learning experience?
No prior AI experience is expected. You should be comfortable with basic programming, ideally in Python. The suggested curriculum starts with backend foundations before moving into LLMs, retrieval and agents. Tell your advisor about your experience to check your starting point.
How do I choose between classroom and live learning?
Choose classroom learning if you prefer an in-person routine and peer interaction. Live learning lets you attend remotely. Ask about current AI FDE batch availability, timings and support before choosing your mode.
What will I practise and build?
The suggested roadmap includes practical assignments and project ideas such as a document Q&A assistant, a workflow automation agent and an end-to-end client deployment. Your mentor will confirm project scope, review criteria and the curriculum for your batch.
What are the course duration, timings and fees?
These depend on your selected batch and learning mode. Contact an advisor for the current start date, class schedule, total duration and fees.
Real people. Real milestones.
Our torchbearers.
Just getting started.
Meet Coding Blocks learners whose practice and persistence led to opportunities at leading companies.
Aditya
Iti Jain
Simarpreet Sikka
Vishesh
Jashpreet Malhotra
Walmart
Sahil Bansal
Shervil Gupta
Ashanka Saha
Sandipan Dutta
Lavisha Malik
Manali Biswas
Mohit K.
CODING BLOCKS CERTIFICATE
Skills you’ve built.
An achievement to show.
Celebrate the work you put into building and deploying AI with a Coding Blocks certificate of completion. Add it to your portfolio and share a milestone in your learning journey. Ask your advisor about the completion criteria for your batch.
-
A record of your completed program
-
A meaningful addition to your portfolio
-
A milestone to share with your network
OUR CLASSROOM CENTRES
Find your classroom.
Right here in Delhi NCR.
Learn with us in Noida or Netaji Subhash Place, Delhi. Find the centre that fits your commute, get directions and come meet the team.
Noida
Sector 2 · Uttar Pradesh
VISIT US
First Floor, A-73, Sector 2,
Noida, Uttar Pradesh 201301
Noida, Uttar Pradesh 201301
CONTACT NUMBER
+91 99995 79111 — call the Noida centre
Near Sector 15 Metro
Get directions to the Noida centre (opens in a new tab)
NSP, Delhi
Netaji Subhash Place · Pitampura
VISIT US
4th Floor, PP Tower, Tower B,
Netaji Subhash Place, Delhi 110034
Netaji Subhash Place, Delhi 110034
CONTACT NUMBER
+91 99993 21553 — call the NSP, Delhi centre
Call us to plan your visit
Get directions to the NSP, Delhi centre (opens in a new tab)
The next wave of AI
needs people who can deploy it.
Let’s find an AI FDE batch that fits where you are and where you want to go.