LIVE ONLINE · INSTRUCTOR-LED FDE PROGRAM
Forward Deployed Engineer (FDE) Course
Learn to turn a customer problem into a working AI system — from discovery and architecture through deployment and handoff.
For working software, data/ML, platform and solutions engineers with basic Python and API knowledge.
Program Summary
- Duration
- 5 months / 20 weeks
- Format
- Live online, instructor-led
- Audience
- Working professionals
- Program fee
- ₹65,000
- Projects
- Applied work + capstone
- Recording access
- 2 years
What You Will Learn in This FDE Course
This course teaches the work of a Forward Deployed Engineer: turning an unclear customer problem into a scoped, integrated and deployed system. You move through discovery, requirements, architecture, AI application engineering, integration, deployment, evaluation and handoff over five months.
Course Fit
Is This FDE Course Right for You?
The course suits working professionals who build software, data or AI systems and want more ownership of customer-facing technical delivery.
Suitable professional backgrounds
- Software, backend or full-stack engineers
- Data, machine learning or AI engineers
- Cloud, DevOps, platform or MLOps engineers
- Solution engineers, technical consultants or architects
What you should already know
- Basic Python programming
- Familiarity with APIs and databases
- General software-development understanding
- Willingness to complete applied assignments and a capstone
This course may not suit you if…
- You want a non-technical overview only.
- You are looking only for prompt-engineering tools.
- You do not plan to complete applied projects.
- You have no programming foundation and need a beginner Python program first.
Want to understand the job before choosing a course? Read what a Forward Deployed Engineer does, then review the FDE roadmap.
Choose another path if you need deeper specialization: the Generative AI Course for fine-tuning and model-side depth, the MLOps Course for the ML lifecycle and CI/CD, or the AIOps Course for broader AI operations and infrastructure.
Skills and Scope
What You Will Learn to Deliver
RAG and agent workflows are options you apply when the problem calls for them — not the definition of the role.
Discovery and success criteria
Turn an unclear client problem into scoped requirements and measurable success criteria.
Solution architecture and integration
Design Python and FastAPI backends and connect them to CRM, ERP, databases and legacy systems through secure APIs.
AI application patterns
Build AI features where they fit — enterprise RAG, and agent workflows with tool use, routing and memory.
Evaluation, guardrails and reliability
Add regression testing, guardrails, tracing and evaluation for quality, latency and cost.
Deployment and observability
Deploy across edge, on-premises, cloud and hybrid environments with monitoring and rollback readiness.
UAT, communication and handoff
Run UAT, present trade-offs to stakeholders and hand off a maintainable system with runbooks.
Curriculum
Forward Deployed Engineer Course Curriculum
The 20-week curriculum follows an FDE engagement from discovery and solution design through AI engineering, deployment, evaluation and handoff.
Build the judgment to choose the right solution approach before writing code.
Core topics
- Choosing between AI, automation, analytics and conventional software — and where ML, NLP, LLM, RAG or agentic patterns fit
- Business workflow mapping: users, systems, handoffs and exceptions
- Pain-point and opportunity analysis tied to business value, KPIs and measurable outcomes
- Data readiness and feasibility assessment
- Python and API application foundations
Evidence produced
- AI approach decision matrix
- As-is workflow map with pain-point register
- KPI and business-value definition
- Baseline Python/API application
Turn a loosely defined client problem into a scoped, costed and presentable approach.
Core topics
- Stakeholder discovery and functional/non-functional requirements
- Assumptions, dependencies, exclusions and risk management
- Data access, ownership, privacy and human-in-the-loop escalation design
- Architecture, data flow, integration and trust boundaries
- Cost and effort estimation, rollout and change control
- Solution proposal presentation and trade-off explanation
Evidence produced
- Discovery report with requirements matrix and KPI definition
- Risk and assumption log
- Solution approach document with architecture diagram
- Rough-order-of-magnitude (ROM) cost and effort estimate with rollout plan
- Solution proposal presentation
Build and integrate the AI application — RAG, agents and system connections applied as the problem requires.
Core topics
- Production-oriented Python/FastAPI services: validation, authentication, async work, persistence and caching
- RAG and retrieval design alongside SQL, structured search and API alternatives
- Tool calling, agent workflows and human-approval steps
- Existing-system integration — CRM, ERP, databases, portals, legacy apps — with RBAC, service accounts and audit trails
- Transaction safety, idempotency and reconciliation
- Evaluation datasets, regression testing, groundedness checks and safety controls
Evidence produced
- Tested AI backend with retrieval or action workflow
- Existing-system integration with identity and permission model
- Evaluation dataset and automated test suite
- UAT scenarios and technical documentation
Deploy the system in a selected environment and evaluate its release readiness and cost.
Core topics
- Packaging, containers and environment configuration
- CI/CD, release gates, secrets and database migrations
- Phased rollout, feature flags and rollback readiness
- Load, concurrency, reliability, latency and cost testing
- Tracing, monitoring, alerting and incident response
- Runbooks, versioning and production improvement
Evidence produced
- Deployment plan with release checklist and rollback plan
- Monitoring dashboard and alert plan
- Production-readiness review
- Support runbook and improvement backlog
Deployment environment decisions
| Environment | Best fit | Main constraint |
|---|---|---|
| Edge | Low-latency, on-device or site-local inference where cloud round-trips are too slow or unreliable. | Constrained by local hardware, model size and update mechanism. |
| On-premises | Regulated or sensitive workloads that must stay inside client infrastructure. | Must adapt to existing identity, security and network policies. |
| Cloud | Scalable workloads, managed services and cross-region delivery. | Ongoing egress and inference costs scale with usage. |
| Hybrid | Mixed workloads where some inference stays local and orchestration runs in the cloud. | Most complex — requires a clear local/cloud boundary and sync design. |
Learners practise deployment in one primary environment and compare how architecture, security, connectivity and handoff requirements change across the other environments.
Demonstrate the solution, run user acceptance testing and prepare a maintainable handoff.
Core topics
- Simulated client demonstration and technical objection handling
- User acceptance testing (UAT), defect and acceptance management
- Operator training and documentation
- Governance, ownership and support escalation
- Business-value review
- Final capstone presentation and handoff
Evidence produced
- Simulated client demo and UAT report
- Architecture and operations documentation with training material
- End-to-end capstone and final FDE presentation
- Handoff pack
Applied Work
FDE Projects and Capstone
You’ll complete three connected project groups, moving from discovery and solution design to building, deploying and handing over an AI system. Projects use realistic scenarios and simulated client interactions; they are not live client deployments.
Discovery and Solution Design
You’ll work on
- Map a simulated client workflow and identify its constraints.
- Turn pain points into requirements, KPIs and acceptance criteria.
- Compare solution options, sketch the architecture and plan delivery.
You’ll produce
- Discovery brief
- Workflow map
- Requirements and KPI sheet
- Architecture diagram
- Solution proposal
Production AI Engineering
You’ll work on
- Build an API-based AI application using RAG or an agent workflow where appropriate.
- Add validation, persistence, fallbacks, audit events and evaluation.
- Connect it to a simulated CRM, ERP, database or legacy system.
You’ll produce
- Working AI application
- API documentation
- Integration evidence
- Evaluation report
- Release notes
End-to-End FDE Capstone
You’ll work on
- Deploy the system in one selected environment.
- Add observability, quality checks and rollback readiness.
- Run UAT, present to a simulated client and prepare the handoff.
You’ll produce
- Deployment plan
- Monitoring dashboard
- UAT report
- Support runbook
- Handoff pack
Learning Experience
How the Live Online FDE Course Works
Live sessions, applied work and simulated delivery exercises run across the five curriculum phases.
Live technical instruction
Interactive online classes connect architecture and engineering decisions to customer requirements.
Applied work
Discovery, architecture, implementation, evaluation and documentation assignments in every phase.
Delivery simulations and capstone
Practise requirement discussions, presentations, UAT and handoff through simulated client scenarios.
Feedback and recordings
Feedback on applied work and capstone reviews. Recordings stay available for two years and supplement live classes.
Enrollment
FDE Course Fee, Certificate and Learner Support
Program details
- Program fee
- ₹65,000
- Duration
- 5 months / 20 weeks
- Format
- Live online, instructor-led
- Recording access
- 2 years
Completion and support
Course-completion certificate
Learners who meet the published completion criteria receive a course-completion certificate issued by School of Core AI.
Project and portfolio guidance
Guidance focuses on explaining architecture, integration, evaluation and delivery decisions demonstrated through course assignments and the capstone.
Career preparation
Resume, interview and system-design support helps learners communicate project depth, technical decisions and trade-offs.
Career support is included, but employment, interviews, placement, salary and other career outcomes are not guaranteed.
Career Directions
Roles That Use Forward-Deployed Engineering Skills
Depending on prior experience and employer naming, relevant job searches may include:
- Forward Deployed Engineer or Forward Deployment Engineer
- AI Solutions Engineer
- Applied AI Engineer
- AI Implementation Engineer
Check the job description for
- Expected coding and system-design depth
- Direct, embedded or remote customer collaboration
- On-site or travel requirements
- Ownership of integration, deployment, adoption and handoff
These roles are possible career directions, not guaranteed outcomes. Fit depends on your experience, portfolio and each employer’s requirements.
FAQ
Forward Deployed Engineer Course FAQs
It is for working software, data, ML, platform and solutions engineers who want customer-facing delivery ownership. You should know basic Python, APIs and databases before joining. The course is not a beginner Python program, and it expects you to complete applied assignments and a capstone.
No. The course teaches AI application patterns, including retrieval and agent workflows, from their foundations. Comfort with basic Python, APIs, databases and general software development is what matters most before you join.
You will complete three project groups: discovery and solution design, production AI engineering, and an end-to-end capstone. Projects use realistic scenarios and simulated client interactions — they are not live client deployments.
Yes. You will build RAG and agent workflows as solution patterns, add evaluation datasets and regression testing, and deploy with monitoring and rollback readiness. The curriculum is broader than generative AI and includes integration and handoff.
Yes. All classes are live and instructor-led, delivered online over five months, or 20 weeks. Class recordings remain available for two years. They supplement live sessions — they do not replace attending.
The ₹65,000 fee covers five months of live online instruction, applied assignments and simulations, capstone reviews, two years of recording access, and a course-completion certificate if you meet the published completion criteria. Career support is included, but employment, interviews, placement and salary outcomes are not guaranteed.
Next Step
Is This FDE Course the Right Next Step?
Review the prerequisites, curriculum and project expectations above. If they match your background and goals, apply for the next cohort.