Forward Deployed
AI Engineering.
A practical cohort for engineers who want to take AI from business ambiguity to production deployment.
Discover
01Scope user processes and frame ROI metrics.
Specify
02Write code specs and test vectors for coding models.
Architect
03Map context pipelines, databases, and model flows.
Build
04Orchestrate agent loops, tools, and custom systems.
Deploy
05Dockerize apps and connect cloud runtimes.
Govern
06Track costs, setup shields, and verify compliance.
Adopt
07Deliver stakeholder demos and map rollout paths.
AI teams do not need more demos.
They need engineers who can own deployment.
Taught and designed by engineers who have delivered Generative & Agentic AI enablement programs directly inside Walmart (Procurement, Cataloging & AP), Novo Nordisk India, and workshops for EY, Hexaware, and Kotak Mahindra Bank.
No generic templates or slide-ware. This program is extracted from live enterprise deployments and provides 9 reusable delivery artifacts, including 3 shippable backend code packages.
Why FDE Now?
AI demos are easy. Enterprise adoption is hard.
Prototypes break under usage, latency, and data limits. Moving from chat boxes to integrated legacy systems is where most AI initiatives stall.
Business problems are rarely well-specified.
Stakeholders define goals as cost savings, not embeddings or APIs. FDEs capture fuzzy needs and convert them into precise specifications.
AI systems need architecture, tests, and observability.
Durable pipelines demand structured RAG logic, automatic evaluation layers, prompt injection shields, and structured observability.
Enterprises need outcomes, not just lines of code.
Traditional engineers build in isolation. FDEs act as delivery owners bridging discovery, specs, development, and user adoption.
This is not another AI tools course.
Compare a standard tutorial path against engineering-level execution.
Normal AI Course
- Prompt engineering shortcuts and tricks
- Basic SaaS tool dashboards and UI demos
- Isolated, toy-like example builds
- Zero ownership of downstream deployment
- No client-side discovery frameworks
FDE Cohort
- Discovery-to-deployment lifecycle ownership
- Spec-driven engineering with coding agents
- Enterprise RAG and agentic workflows
- Evaluation, security boundaries, and telemetry
- A complete Capstone deployment blueprint
Who is a Forward Deployed AI Engineer?
A Forward Deployed AI Engineer sits at the intersection of software engineering, AI solutioning, consulting, deployment, and adoption ownership.
Software Engineer
Focuses on writing code, standard features, and core backend endpoints.
An FDE owns the business scoping, architecture, specs, and final system integration.
AI Engineer
Trains models, fine-tunes parameters, and creates specialized math layers.
An FDE packages these models, connects them to corporate APIs, and handles constraints.
Solution Architect
Designs cloud blueprints, system topologies, and generic network shapes.
An FDE builds and runs working prototypes to prove feasibility in fast user feedback loops.
Consultant
Frames business challenges and presents strategic roadmap proposals on slides.
An FDE writes code, configures specs, deploys Docker services, and proves actual ROI.
FDE Operating Model
The 8 core disciplines orchestrated by a Forward Deployed Engineer
What You Will Actually Build
Concrete system prototypes built backwards from industry failure scenarios
AI Opportunity Brief
Vague, unstructured business problem or operational bottleneck.
Structured brief detailing workflows, model feasibility, and ROI indicators.
Stakeholder & Workflow Map
Fragmented manual corporate process executed across tools.
Interactive user journey identifying AI injection coordinates and latencies.
Spec-Driven AI Feature
Feature requirement description or high-level idea.
PRD with acceptance criteria, unit test cases, and model task instructions.
RAG Knowledge Assistant
Mass of raw, unstructured company documents and files.
Deployed retrieval system with semantic chunking and citation scoring.
Agentic Workflow Prototype
Multi-step task requiring external API tools and user checkpoints.
Working multi-agent flow executing tasks with deterministic fallbacks.
Deployment & Governance Pack
Raw, unoptimized prototype AI script.
Dockerized production service with token cost logging and telemetry tracing.
Artifact-Led Outcomes
Deliverables you will produce and own:
AI Opportunity Brief
Stakeholder Discovery Map
SDD Spec Pack
RAG / Agent Architecture
Working AI Workflow Prototype
Evaluation and Governance Checklist
Deployment Readiness Pack
FDE-style Capstone Demo
Curriculum Overview
Understand the FDE operating model, why the role is emerging in 2026, and the enterprise constraints under which FDEs deliver.
Key Topics Covered
- What is Forward Deployed AI Engineering?
- AI pilot failure patterns in the enterprise (Harvard & MIT framework teardown)
- FDE responsibilities across discovery, build, and adoption
- Enterprise constraints: security, compliance, telemetry, costs, and change management
Hands-on Labs + Capstone
9 Core Training Labs
Build a reusable framework library of FDE tools throughout the cohort modules.
From Business Problem to Deployed AI Solution
Participants work on a realistic enterprise scenario and move through the complete FDE loop: running stakeholder discovery, designing vector and RAG schemas, coding with spec packs, deploying, setting up evaluation rigs, and drafting a 90-day adoption roadmap.
Capstone Deliverables
Platform & Tool Stack
Grouped categories covering modern developer toolchains and runtime models.
AI Models & Assistants
AI Coding Agents
Specification & SDLC
Engineering Stack
Governance
Built for Enterprise AI Delivery
Designed by Practitioners
Built by experts who actively train Fortune 500 engineering and product teams.
Real-World Constraints
Designed around core enterprise SDLC, deployment security, and governance limits.
Templates & Playbooks
Access the exact PRD specs, checklist logs, and templates used on corporate contracts.
Customizable for Teams
Modular pathways adapt training scenarios to your exact internal tool stack and cloud.
Enterprise Deployment Options
Public Cohort Seats
Enroll individuals in scheduled weekend cohorts with other builders.
Private Corporate Cohorts
Dedicated team sessions with custom dates, pace schedules, and reviews.
Custom Domain Cases
Align practical labs with your custom internal data flows and processes.
Capstone Stack Alignment
Map prototype tests and setups directly to your internal cloud stack.
Who Should Attend
Developers
Tech Leads
Solution Architects
Consultants / BAs
QA Automation
Delivery Managers
Recommended Background
Basic programming scope (JS, Python, etc.), high-level API usage understanding, command line/Git awareness, and standard SDLC familiarity.
Not Required
Advanced machine learning fine-tuning, complex deep mathematics, deep neural networks code, or prior cloud system orchestration mastery.
Program Format & Pricing
Syllabus Format
Launch Individual Enrollment
₹26,999 + GST
- 30 live instructor-led hours
- 7 structured syllabus modules
- 9 hands-on delivery artifacts
- Capstone project review
- Templates and playbooks
- AIXL Academy certificate
- Optional SDD bundle: complete pathway for ₹39,999 + GST total (save ₹11,000)
Launch cohort pricing is available for the first public cohort only.
Corporate Edition
Custom Corporate
Custom pricing based on team size, tools, domain use cases, cloud labs, and capstone customization.
- Client stack & codebase mapping
- On-prem deployment configurations
- Internal business scenario capstones
- Flexible dates and timing options
- Enterprise security & SSO setup
Simple Cohort Admission Process
Apply
Fill out a quick form detailing your engineering background.
Fitment review
Our team validates if this cohort pace aligns with your profile.
Confirm seat
Secure your enrollment prior to the cohort limit.
Join onboarding
Setup sandbox credentials and tools checklists.
Start cohort
Launch into module lectures, active reviews, and capstone labs.
Enrollment FAQ
Everything you need to know about the FDE program
Build engineers who can take AI from demo to deployment.
Join the launch cohort and learn the operating model behind practical Forward Deployed AI Engineering.