SCHOOLOFCOREAI

LIVE ONLINE · INSTRUCTOR-LED FDE PROGRAM

Forward Deployed Engineer Course

Learn to take an ambiguous client problem from discovery and solution design through engineering, integration, deployment, monitoring and handoff.

5 Months
20-week learning path
Live Instructor-Led
online sessions
₹65,000
program fee
2-Year Recording Access
review anytime
Apply for the Next Cohort

Designed for software, backend, full-stack, ML, data, computer vision, GenAI and solution engineers.

FDE Delivery Lifecycle

The program follows the way a real AI engagement moves from discovery to production and handoff.

  1. 01

    Discover the Client Workflow

    Identify users, decision points, workflows, pain points, systems, constraints and data before choosing any AI pattern.

  2. 02

    Define the Problem and Success Criteria

    Clarify business value, KPIs, scope, acceptance criteria and risks so the team can judge usefulness objectively.

  3. 03

    Design the Solution Architecture

    Choose the right system pattern—LLM app, RAG, agent or structured orchestration—based on workflow needs, reliability expectations and deployment environment.

  4. 04

    Explain Trade-offs and the Delivery Plan

    Present the proposal, architecture trade-offs, cost, latency and rollout plan to stakeholders and secure alignment.

  5. 05

    Execute the Build and Integration

    Build the application layer, integrate with existing business systems, test against realistic usage and tighten the system before expansion.

  6. 06

    Operate, Evaluate and Improve

    Monitor quality, latency, cost and errors; capture system behaviour; troubleshoot; and improve the workflow after launch.

  7. 07

    Hand Off the Production System

    Complete UAT evidence, training, documentation, runbooks and ownership transfer so the system stays trustworthy over time.

Program at a Glance

Duration
5 months / 20 weeks
Format
Live online
Delivery
Instructor-led
Program fee
₹65,000
Recording access
2 years
Projects and capstone
Applied projects and an end-to-end capstone
Certificate
School of Core AI course-completion certificate
Portfolio and career support
Portfolio guidance and career-support direction

What Does a Forward Deployed Engineer Do?

A Forward Deployed Engineer works directly with customers, users or internal business teams to understand operational problems, map workflows, define success criteria, design the right technical approach, build and integrate the solution, deploy it in the required environment, collect feedback, operate and improve the system, and complete production handoff. The role combines client discovery, solution architecture, engineering execution and production ownership.

An FDE is not only a GenAI developer. RAG, agents, ML, computer vision and automation are solution patterns used when the problem requires them.

FDE Roadmap

Forward Deployed Engineer Roadmap: From Client Problem to Production

The roadmap below is how this course approaches forward-deployed AI delivery: business context first, production readiness last, and measurable validation in between.

01

Discover the Client Workflow

Identify users, decision points, workflows, pain points, systems, constraints and data before choosing any AI pattern.

02

Define the Problem and Success Criteria

Clarify business value, KPIs, scope, acceptance criteria and risks so the team can judge usefulness objectively.

03

Design the Solution Architecture

Choose the right system pattern—LLM app, RAG, agent or structured orchestration—based on workflow needs, reliability expectations and deployment environment.

04

Explain Trade-offs and the Delivery Plan

Present the proposal, architecture trade-offs, cost, latency and rollout plan to stakeholders and secure alignment.

05

Execute the Build and Integration

Build the application layer, integrate with existing business systems, test against realistic usage and tighten the system before expansion.

06

Operate, Evaluate and Improve

Monitor quality, latency, cost and errors; capture system behaviour; troubleshoot; and improve the workflow after launch.

07

Hand Off the Production System

Complete UAT evidence, training, documentation, runbooks and ownership transfer so the system stays trustworthy over time.

Technical Scope

Technical Scope: AI Applications, RAG, Agents and LLMOps

RAG, agents, ML, computer vision, workflow automation and conventional software are solution patterns. A Forward Deployed Engineer chooses and combines them according to the client problem, operating environment, value, data, risk, cost and deployment constraints.

AI Application Engineering and APIs

Build production-oriented AI application backends using Python, FastAPI, structured outputs, validation, authentication, async tasks, queues, persistence and caching. APIs connect the AI system to existing business workflows and legacy systems.

Enterprise RAG Systems

Design retrieval-augmented generation for enterprise knowledge workflows—ingestion, chunking, embeddings, retrieval, reranking, citations and grounded answers. Learn SQL, structured search and API alternatives, and evaluate groundedness, relevance and task completion.

Agentic AI Workflows

Build agent workflows with tool use, routing, memory and business-process automation logic. Connect agentic patterns to operational task execution with human approval and escalation design.

Multi-Agent Orchestration

Design multi-agent systems where specialised agents collaborate across planning, retrieval, execution and validation steps. Multi-agent workflows appear where the curriculum genuinely teaches orchestration patterns for business automation.

LLMOps, Evaluation and Observability

Add evaluation datasets, regression testing, tracing, logging, latency and cost monitoring. Connect LLMOps to production accountability—quality, error and cost monitoring, incident response and continuous improvement after go-live.

Production Integration and Handoff

Integrate with CRM, ERP, database, portal and legacy systems. Apply RBAC, service accounts, audit trails, transaction safety and idempotency. Prepare UAT scenarios, release scorecards, documentation and runbooks for ownership transfer.

The course is deliberately scoped around forward-deployed AI delivery. For deeper model-side GenAI work, fine-tuning and model serving, explore the Generative AI Course. For ML lifecycle, CI/CD and model registry depth, explore the MLOps Course. For broader AI operations and infrastructure, explore the AIOps Course.

Audience Fit

FDE Course Prerequisites and Who Should Join

The program is designed for professionals who want stronger capabilities in solution discovery, architecture, implementation, deployment and client ownership. Working professionals can join alongside full-time learners.

Suitable audiences

  • Software engineers
  • Backend developers
  • Full-stack developers
  • ML engineers
  • Data scientists
  • Computer vision engineers
  • GenAI engineers
  • Solution engineers
  • Technical consultants
  • Working professionals moving toward client-facing AI delivery

Prerequisites

  • Basic Python programming
  • Familiarity with APIs and databases
  • General software-development understanding
  • Willingness to complete applied assignments and a capstone

Confirm exact prerequisites with the admissions team before enrolling. Do not invent degree, experience or programming requirements.

This may not be the right course if…

  • The learner wants a non-technical overview only.
  • The learner is looking only for prompt-engineering tools.
  • The learner does not plan to complete applied projects.
  • The learner has no programming foundation and needs a beginner Python program first.

These statements are published only after confirming they match the admissions policy.

Check If This Course Fits Your Background

Learner Outcomes

What You Will Be Able to Do as a Forward Deployed Engineer

01

Discover and Scope Client Problems

Convert a vague client request into a measurable use case, success criteria and delivery boundary.

02

Select and Justify a Solution Approach

Compare AI, software, data, rules, retrieval, agents and automation patterns and justify the chosen approach.

03

Design and Explain the Architecture

Create architecture, integration, security, cost, rollout and risk plans and explain them to stakeholders.

04

Build and Integrate the AI System

Develop maintainable services and connect them to existing business systems.

05

Deploy, Evaluate and Operate

Prepare systems for edge, on-premise, cloud or hybrid production environments and operate them after launch.

06

Demonstrate and Hand Off

Complete UAT, documentation, training, monitoring and operational ownership transfer.

Curriculum

Five-Month Forward Deployed Engineer Curriculum and Syllabus

The 20-week learning path follows the way a real AI engagement moves from discovery to production and handoff.

Topics

  • AI, automation, analytics and conventional software decisions
  • ML, computer vision, NLP, LLM, RAG and agentic solution patterns
  • Business workflows, users, systems, handoffs and exceptions
  • Pain-point and opportunity analysis
  • Business value and measurable outcome definition
  • Data readiness and feasibility
  • Python application and API foundations

Outputs

  • AI approach decision matrix
  • As-is workflow map
  • Pain-point register
  • AI opportunity canvas
  • Baseline application

Projects and FDE Artifacts

Forward Deployed Engineer Projects and Capstone

Project 1

Client Discovery and AI Opportunity Assessment

Learner work

  • Conduct a discovery simulation
  • Map the current process
  • Identify pain points and constraints
  • Define KPIs
  • Recommend where AI should and should not be used

Artifacts

  • Discovery report
  • Workflow map
  • Opportunity canvas
  • KPI definition
  • Risk register

Project 2

Solution Approach and Architecture Proposal

Learner work

  • Compare possible approaches
  • Select the environment
  • Create a high-level architecture
  • Estimate delivery and operating cost
  • Present the proposal

Artifacts

  • Decision matrix
  • Architecture diagram
  • Cost estimate
  • Rollout plan
  • Client presentation

Project 3

Production AI Application with RAG

Learner work

  • Build an API-based AI application with retrieval-augmented generation
  • Add persistence, validation, fallback and audit events
  • Apply testing and configuration management

Artifacts

  • Working application with RAG
  • API documentation
  • Test evidence
  • Configuration and release notes

Project 4

Agentic Workflow and Existing-System Integration

Learner work

  • Build an agentic workflow with tool use and routing
  • Integrate with a CRM, ERP, database, document system, portal or simulated legacy application

Artifacts

  • Integration map
  • Working connector
  • Identity and permission model
  • Failure and recovery flow

Project 5

Deployment, Evaluation and Monitoring

Learner work

  • Deploy in one primary environment
  • Explain design changes for edge, on-premise, cloud and hybrid environments
  • Add observability, evaluation and rollback readiness

Artifacts

  • Deployment pack
  • Monitoring dashboard
  • Release checklist
  • Rollback plan
  • Support runbook

Project 6

End-to-End FDE Capstone and Production Handoff

Learner work

  • Deliver the complete engagement from client discovery through handoff.

Artifacts

  • Discovery report
  • As-is workflow
  • Use-case definition
  • KPIs and acceptance criteria
  • Approach comparison
  • Architecture and integration design
  • Security and cost considerations
  • Working AI system
  • Evaluation and UAT evidence
  • Deployment pack
  • Monitoring dashboard
  • Rollback plan
  • Support runbook
  • Client presentation
  • Handoff documentation

Deployment Environments

Deployment Environments You Will Work With

Edge

When suitable
Low-latency, on-device or site-local inference where round-trip to cloud is too slow or unreliable.
Data & privacy
Data stays on-device or on-site; lower exposure to transit-based risks.
Latency
Lowest latency; depends on local hardware constraints.
Connectivity
Works with intermittent or no connectivity.
Cost
Higher per-unit hardware cost; lower ongoing cloud egress cost.
Ownership
Operational ownership sits with the client site or device team.
Integration
Constrained by local runtime, model size and update mechanism.

On-Premise

When suitable
Regulated or sensitive workloads that must stay inside client infrastructure.
Data & privacy
Data remains within the client network boundary.
Latency
Network latency depends on internal infrastructure.
Connectivity
Requires internal network; no public internet dependency for inference.
Cost
Client bears infrastructure and maintenance cost.
Ownership
Client owns operations; FDE defines handoff and runbooks.
Integration
Must adapt to existing identity, security and network policies.

Cloud

When suitable
Scalable workloads, managed services and cross-region delivery.
Data & privacy
Data transits to and resides in the chosen cloud region.
Latency
Depends on region, endpoint and network path.
Connectivity
Requires reliable internet connectivity.
Cost
Pay-as-you-go or reserved; egress and inference costs scale with usage.
Ownership
Shared responsibility between provider and your team.
Integration
Broadest managed-service integration options.

Hybrid

When suitable
Mixed workloads where some inference stays local and orchestration lives in cloud.
Data & privacy
Sensitive data stays local; non-sensitive processing can use cloud.
Latency
Tiered: local for hot paths, cloud for cold or batch paths.
Connectivity
Requires connectivity for cloud-managed components.
Cost
Balanced; optimise per workload tier.
Ownership
Split ownership across local and cloud components.
Integration
Most complex; requires clear boundary and sync design.

Learning Experience

How the Five-Month Live Online FDE Course Is Delivered

Live Instructor-Led Classes

Learn through live online sessions that connect technical decisions to client requirements, business outcomes and production constraints. The course is not self-paced.

Guided Applied Work

Apply each phase through architecture exercises, implementation tasks, evaluation evidence and delivery documentation.

Client Delivery Simulations

Practise discovery, solution presentations, objection handling, UAT discussions and technical handoff.

Two-Year Class Recording Access

Review class recordings for 2 years from the approved access start point. Recordings supplement live classes—they do not replace them.

Capstone Reviews and Instructor Feedback

Bring discovery, architecture, engineering, deployment, monitoring and client communication together in one complete FDE engagement with instructor feedback.

Do not interpret the above as individual mentorship, lifetime support, unlimited doubt sessions or one-to-one reviews unless explicitly confirmed by the admissions team.

Fee and Enrollment

FDE Course Fee, Format and Two-Year Recording Access

Fee
₹65,000
Duration
5 months / 20 weeks
Format
Live online
Delivery
Instructor-led
Recording availability
2 years
Certificate
School of Core AI course-completion certificate, subject to verified completion criteria
Apply for the Next Cohort

No fake crossed-out price, countdown timer, limited-seat count or EMI amount is shown. Confirm taxes, installment options and refund policy with the admissions team.

Certificate and Career Support

Forward Deployed Engineer Certificate, Portfolio and Career Support

Forward Deployed Engineer Certificate

Learners receive a School of Core AI course-completion certificate, subject to the verified completion criteria. It is not described as a government, university, IIT, regulated, accredited or internationally recognized certificate.

Project Portfolio Guidance

Support for shaping project stories around architecture, delivery decisions and business impact. Learners build evidence of end-to-end FDE delivery.

Resume, Interview and System Design Support

Help in presenting the right project depth, explaining system choices clearly and practising system design discussions for AI delivery interviews.

Solution Communication and Handoff Practice

Practice discussing architecture decisions, trade-offs and handoff with stakeholders, hiring teams and customers.

Career support is included. The program does not guarantee employment, interviews, placement, salary or career outcomes.

Role Comparison

Forward Deployed Engineer vs AI Engineer, AI Developer and AI Solutions Engineer

Forward Deployed Engineer

Primary responsibility
Takes a client problem from discovery through production handoff.
Client proximity
Works directly with customers, users or internal business teams.
Technical scope
AI apps, RAG, agents, multi-agent orchestration, LLMOps, evaluation, observability and existing-system integration.
Deployment responsibility
Owns deployment decisions across edge, on-premise, cloud and hybrid environments.
Handoff / ongoing support
Completes UAT, documentation, training, monitoring and ownership transfer.

AI Engineer

Primary responsibility
Builds model-aware AI systems using ML, deep learning, LLMs, fine-tuning and model serving.
Client proximity
Closer to the model and platform than to the client workflow.
Technical scope
ML, deep learning, multimodal AI, model serving and infrastructure.
Deployment responsibility
Focuses on model deployment and serving infrastructure.
Handoff / ongoing support
May hand off to application or platform teams for ongoing operation.

AI Developer

Primary responsibility
Builds AI applications using LLM APIs, RAG, backend workflows and integrations.
Client proximity
Closer to the product and application layer than to the client.
Technical scope
LLM applications, RAG, APIs and backend AI workflows.
Deployment responsibility
Deploys application services; may not own production operations.
Handoff / ongoing support
Typically hands off to operations or SRE teams.

AI Solutions Engineer

Primary responsibility
Translates business needs into AI solution designs and integration plans.
Client proximity
Works with business stakeholders to define the solution.
Technical scope
Solution design, architecture and integration planning.
Deployment responsibility
Defines deployment approach; may not build or operate the system.
Handoff / ongoing support
Hands off design to engineering teams for implementation.

Career Direction

Roles This Forward Deployed Engineer Course Can Help You Prepare For

Roles this course can support

  • Forward Deployed Engineer
  • Forward Deployed AI Engineer
  • AI Solutions Engineer
  • AI Implementation Engineer
  • Production AI Engineer
  • LLM Application Engineer
  • Agentic AI Engineer
  • AI Developer

The learner’s portfolio can include:

  • Discovery documents
  • Workflow maps
  • Architecture decisions
  • Cost and rollout plans
  • Application code
  • Integration evidence
  • Evaluation results
  • Deployment evidence
  • Monitoring dashboards
  • UAT documentation
  • Client presentation
  • Handoff pack

Career outcomes depend on your background, portfolio quality, interview preparation and hiring market conditions. This course does not guarantee employment, interviews, placement or salary.

FAQ

Forward Deployed Engineer Course — Frequently Asked Questions

A Forward Deployed Engineer course prepares engineers to take a client problem from discovery and solution design through engineering, integration, deployment, monitoring and handoff. It combines client discovery, solution architecture, AI engineering, production operations and client delivery.

Next Step

Ready to Build Client-Ready Production AI Systems?

Join the five-month Forward Deployed Engineer course and learn to move from client discovery to production deployment, operation and handoff.

Talk to a Course AdvisorView the Five-Month Curriculum

Get the complete curriculum, fee details and project list.

Discuss whether this program fits your background and goals.

Review the 20-week phase-by-phase learning path.