What Is the Executive Post Graduate Programme in Forward Deployed AI Engineering?
Key Highlights: Executive Post Graduate Programme in Forward Deployed AI Engineering
Why Choose the IIT Kharagpur Executive Post Graduate Programme in Forward Deployed AI Engineering?
Building an AI model is only one part of an AI project. The forward deployed AI engineer course helps engineers to understand the problem, design its solution which fits the client, deploy it and support its use.
Some of the key features of this forward deployed engineer course are:
Client-facing engineering: Discover your study topics from the beginning to end, identify all the stakeholders, figure out your scope of work, understand the contract terms.
Using constraints as a starting point: First figure out what your design is going to constrain affecting an organization's operations. Then pick your architecture.
Applied learning: Solve problems related to Artificial Intelligence modeling, evaluation, edge deployment, and operations.
Demonstration and communication: Learn how to explain your technical choices, present unfinished systems, and deal with stakeholder objections.
Live client capstone: Solve the problem statement provided by the client or its equivalent version.
Campus immersion: Visit IIT Kharagpur campus for one day which will involve Technical Master Class, Networking, and Capstone Demonstration.
This forward deployed engineer certification is basically for those engineers who already have technical experience and want to take more ownership of client facing AI projects.
What Will You Learn in the Forward Deployed AI Engineer Course?
This FDE course syllabus covers five modules along with a capstone project with a duration of 28 weeks. The FDE course syllabus from IIT Kharagpur consists of these modules:
Orientation: The Forward Deployed Engineer Role
Week 1 | 3 live hours
Topics Covered:
Forward Deployed Engineer responsibilities
Role scope and client delivery
Technical communication
Skills Acquired:
Understanding the FDE role
Explaining technical concepts clearly
Connecting engineering work with client needs
Course 1: Applied AI Systems: Modelling and Evaluation
Topics Covered:
Problem formulation and model selection under operational constraints
Time-series modelling and anomaly detection for sensor streams
Predictive maintenance and remaining useful life estimation
Computer vision for automated quality inspection
RAG over operational documents and data
Agentic systems, tool use, orchestration, and verifiable execution
System evaluation, cost modelling, and safety classification
Skills Acquired:
Selecting AI approaches based on the problem and operating conditions
Analysing sensor data and identifying anomalies
Developing approaches for predictive maintenance and visual inspection
Building retrieval-based AI applications
Understanding bounded agentic workflows
Evaluating system costs, performance, and safety
Course 2: Requirements Engineering and Solution Architecture
Topics Covered:
Requirements elicitation and stakeholder discovery
Stakeholder analysis and organisational dynamics
Solution architecture and constraint-driven design
Scope definition and statements of work
Value hypotheses and client requirements
Skills Acquired:
Conducting structured client discovery sessions
Identifying decision-makers, influencers, and blockers
Separating the stated problem from the underlying business need
Scoping solutions against technical and operational constraints
Preparing solution briefs and statements of work
Connecting proposed solutions with measurable business value
Course 3: Data Engineering, Edge Deployment, and Operational Technology Security
Topics Covered:
Connecting data across IT and operational technology systems
Edge inference under intermittent connectivity
Production operations, monitoring, and observability
Operational technology security, identity, and adversarial robustness
Regulatory compliance, data sovereignty, and safety cases
Skills Acquired:
Planning data flows across enterprise and operational systems
Assessing AI deployment needs where connectivity is limited
Monitoring production systems for failures and performance changes
Understanding operational technology security risks
Planning AI deployments around data protection and compliance requirements
Accounting for security and safety in enterprise deployments
Course 4: Demonstration, Adoption, and Operational Handover
Topics Covered:
Operator workflow design and demonstration engineering
Executive communication and presenting uncertainty
Objection handling and expectation management
Adoption, handover, and the product feedback loop
Skills Acquired:
Designing demonstrations around user workflows
Explaining technical decisions to non-technical stakeholders
Presenting system limitations clearly
Responding to objections without overpromising
Managing adoption and handover
Sharing recurring client feedback with product teams
Course 5: The Running Deployment Engagement
The programme schedule includes activities related to engagement hardening, professional practice, and the final capstone demonstration.
Topics Covered:
Engagement hardening and defence preparation
Professional practice through a hiring-loop simulation
Capstone defence and demonstration
Skills Acquired:
Preparing an AI deployment for final review
Explaining technical and business decisions
Demonstrating a working solution
Responding to questions from practitioners and hiring managers
Capstone: Complete End-to-End AI Deployment
The capstone project in this forward deployed engineer course will ask you to take a real client brief and run a complete forward-deployed engagement. Teams will first identify the challenge, solve the problem, make a prototype, and finally present their solution to a panel of practitioners and recruiters.
The capstone involves a one-day immersion on campus at IIT Kharagpur.
Capstone Focus:
Conduct a client discovery session and prepare a discovery brief
Develop a solution brief and statement of work
Build a working prototype with a documented repository
Prepare an architecture diagram and demonstration video
Complete a Digital Personal Data Protection compliance audit
Develop a cost model in Indian rupees
Present the solution during Demo Day and respond to questions
Skills Acquired:
Managing an AI engagement from discovery to deployment
Building solutions around real client constraints
Documenting architecture and technical decisions
Assessing compliance and deployment costs
Demonstrating business value to non-technical stakeholders
Defending technical choices and handling objections
The capstone is evaluated on client problem accuracy, solution quality, prototype performance, demonstration and communication, and question handling.
Who Should Enrol in the Executive Post Graduate Programme in Forward Deployed AI Engineering?
This forward deployed AI engineer course fits you best if you are:
Software Engineers
If you have worked on building APIs, services or software products, the program teaches you client discovery, AI deployment, solution scoping and demonstration engineering.
AI and Machine Learning Engineers
Someone working on creating ML models or pipelines but have never got chance to own the entire production pipeline, this course helps them by including topics such as evaluation, deployment, operations in production, security and handover.
Pre-Sales Engineers and Solutions Architects
If you are involved in working with client and presenting technical demonstrations, this programme will help you to develop your ability to build, deploy and also defend your complete AI solutions.
Computer Science Graduates with Strong Project Experience
This program also targets high-intent computer science graduates and engineers who have less than two years experience as the second audience category. They need to have self-study initiative, project work and coding skills.
Eligibility Criteria for the Executive Post Graduate Programme in Forward Deployed AI Engineering
Eligibility criteria for the Executive Post Graduate Programme in Forward Deployed AI Engineering are:
Bachelor’s or master’s degree in Computer Science, Engineering, or any technical field
Minimum one-year work experience in software engineering, AI & ML engineering, or solutions engineering
Python programming skills
Minimum one shipped REST API or Data Pipeline project
IIT Kharagpur Forward Deployed AI Engineering Programme Fees Structure
The cost for this forward deployed AI engineer course is a single fee structure covering all modules, a capstone project and 1-Day campus immersion at IIT Kharagpur.
The fee structure for this FDE course IIT is: