◆ Enterprise AI Capability Programme · 2026
Build a Bench of Forward Deployed Engineers Inside Your Organisation
Programme At A Glance
- Format — VILT + in-person labs
- Cohort — 15–25 engineers
- Duration — Half-day to multi-week
- Capstone — Live client scenario
- Tailoring — Built to your stack
- Credential — Skillopedia FDE Certificate
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500+
Corporates trained on AI
$238K
5,330
GOOGLE · MICROSOFT · AMAZON · META · ACCENTURE · DELOITTE. SIEMENS · SAMSUNG · MUFG Bank & more
◆ Why This Role · Why Now
Foundation models are commoditised. Deployment is the bottleneck.
The fastest-growing AI role
FDE postings jumped roughly 729% year-on-year — from 643 to 5,330 US listings between April 2025 and April 2026. India GCCs in Bengaluru and Hyderabad are adding FDE-shaped roles at pace.
A measurable salary premium
Global FDE median total compensation sits around $238K, with senior roles clearing $450K+. In India, bands run ₹18–28L entry, ₹30–50L mid, and ₹50–80L+ for senior.
The skill your team is missing
Most engineers can build a demo. Very few can run a discovery conversation, frame the right problem, ship a production-grade system, and drive its adoption. That full-lifecycle ownership is what we train.
◆ The FDE Profile
A Forward Deployed Engineer is six professionals in one
AI Engineer
Builds production RAG, agents, and LLM integrations on real infrastructure.
Business Consultant
Extracts the real requirement from the stated one, and pushes back on scope without losing the room.
Solution Architect
Maps a business need to the right AI pattern — RAG, agents, fine-tuning, or classical ML.
Automation Builder
Wires AI into existing enterprise workflows and systems so it actually saves time.
Client Communicator
Translates technical capability into business value for non-technical stakeholders.
Adoption Driver
Owns the rollout, change management, and the metrics that prove ROI after launch.
◆ The Transformation
Where your team is now vs. where they'll be
Day Zero — where most teams start
- Capable developers, but unsure how to scope an AI engagement from a discovery conversation
- Comfortable in Python, but no production LLM, RAG, or agent experience
- Can run a prototype locally, but can't take it to an observable, cost-aware, secure service
- No structured method for problem framing or AI-pattern selection in a client setting
- Can demo AI, but can't drive adoption or prove ROI to a business stakeholder
Programme Close — where they'll arrive
- Runs a discovery workshop and produces a fundable solution sketch within days
- Builds production RAG and agentic systems on the cloud your business actually uses
- Ships an end-to-end service with monitoring, guardrails, and cost controls
- Selects the right AI pattern deliberately and defends the trade-offs
- Delivers a 90-day adoption plan and becomes the AI champion in their accounts
◆ Session Objectives & Learning Outcomes
What the programme sets out to do — and what your team walks away able to do
Session Objectives (SO)
- Establish a shared mental model of the FDE seat across the full discovery-to-adoption lifecycle
- Build fluency in the 2026 LLM landscape and when each AI pattern earns its place
- Develop a repeatable methodology for client discovery, problem framing, and use-case scoping
- Engineer production-grade RAG and agentic systems on enterprise cloud infrastructure
- Operationalise AI with observability, guardrails, cost control, security, and red-teaming
- Equip engineers to lead adoption, change management, and ROI measurement post-launch
Learning Outcomes (LO)
- Run a discovery conversation that produces a scoped, fundable solution sketch
- Translate an ambiguous business need into the correct AI architecture
- Build and deploy a grounded RAG pipeline with retrieval, re-ranking, and evaluation
- Design and ship a multi-agent workflow that completes a real business task
- Instrument, monitor, secure, and cost-optimise an AI service in production
- Present an AI solution and a 90-day adoption plan to business stakeholders
◆ Curriculum
A modular syllabus, built to be tailored
- How LLMs actually work — GPT, Claude, Llama and the 2026 frontier-model landscape
- Prompt engineering and systematic evaluation with eval harnesses
- AI workflows and where Generative AI creates business transformation
- Lab: map your own business units to FDE engagement archetypes
- APIs, integrations, and connecting LLMs to real company systems
- Cloud fundamentals, data pipelines, and AI solution architecture
- Security, governance, and data-residency foundations for enterprise
- From business need to AI pattern — a decision framework
- Multi-agent systems and autonomous workflows
- Tool calling, orchestration, and AI assistants
- Hands-on: take a single-agent prototype to a supervised multi-agent workflow
- Workflow automation that wires AI into existing enterprise processes
- Retrieval-Augmented Generation: chunking, embeddings, retrieval, grounding
- Vector databases and enterprise search systems
- Document intelligence and knowledge assistants
- Hybrid retrieval with re-ranking — the pattern most real engagements ship
- Internal copilots and sales assistants
- HR AI systems and finance automation
- Customer-support AI and meeting-intelligence systems
- Production concerns: streaming, auth, rate limits, caching, model routing
- Discovery workshops, stakeholder management, and client communication
- AI implementation strategy and adoption frameworks
- Observability, guardrails, and responsible-AI / red-teaming basics
- Change management and the 90-day adoption playbook
- Each engineer delivers a deployed RAG or multi-agent service against a real brief
- Discovery doc, solution architecture, monitoring view, and a 90-day adoption plan
- Evaluated by Skillopedia and, optionally, your own client-side panel
- Earns the Skillopedia Forward Deployed Engineer certificate
◆ Hands-On Projects
Your team builds real systems, not classroom exercises
AI Customer Support Agent
A grounded, tool-using agent that resolves real tickets end-to-end.
AI Sales Analyzer
Turns raw sales data into insight and next-best-action recommendations.
Enterprise Knowledge Assistant
RAG over internal documents with citations and access control.
AI HR Recruiter
Screens, summarises, and shortlists against a real role spec.
Meeting Intelligence System
Transcribes, extracts actions, and routes follow-ups automatically.
Workflow Automation Platform
Multi-step automation that connects AI to existing business systems.
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◆ Delivery Framework
A four-milestone path from skill gap to client-ready
Stage One — Foundations & Discovery
The FDE seat, the 2026 LLM landscape, prompt engineering with evals, and a graded discovery doc for a mock brief.
Stage Two — RAG & Agents
Production RAG with hybrid retrieval and re-ranking, then multi-agent workflows on your cloud of choice.
Stage Three — Production & Security
Deploy a real service with streaming and auth, instrument it with observability, and run a red-team pass.
Stage Four — Capstone & Adoption
A full client-scenario delivery plus a 90-day adoption plan, evaluated by a panel.
◆ Corporate vs. Generic Courses
Why enterprise teams choose a tailored engagement
| What’s covered | Generic Online Course | Skillopedia Corporate FDE |
|---|---|---|
| Client discovery & use-case scoping | Not covered | Full discovery-workshop facilitation & MVP framing |
| Production RAG engineering | Introductory concepts only | Hybrid retrieval, re-ranking, grounding, evaluation |
| Agentic AI & orchestration | Single-framework overview | Hands-on multi-agent workflow labs |
| Deployment environment | Local / sandbox only | Built on your enterprise cloud & tech stack |
| Security & red-teaming | Not included | Guardrails, data residency, responsible-AI pass |
| Certification | MCQ / quiz-based | Panel-evaluated capstone credential |
| Adoption & change management | Not included | 90-day adoption plan & champion network design |
| Tailoring to your business | One-size-fits-all | Rebuilt per engagement around your outcomes |
HM
Hitesh Motwani
◆ Your Lead Trainer
Taught by someone who has actually shipped AI into enterprises
- Visiting faculty at 9 IIMs and the University of London
- Trained 2,00,000+ professionals across 16+ countries
- Author of Generative AI 360° — a complete enterprise GenAI playbook
- Practitioner roots across BFSI (HDFC, HSBC, DSP BlackRock), advertising (Ogilvy, Rediffusion Y&R), and insurance
- Led digital for Ratan Tata and the Twitter India launch — credibility across pharma, FMCG, manufacturing & tech
- Delivers Anthropic's full Claude ecosystem — Models, Projects, Agent Skills, Connectors & Agentic AI
◆ Who This Is For
Built for L&D leaders and their teams
Ideal participants
- Software engineers and tech leads moving into client-facing AI roles
- Consultants, product managers, and delivery leads owning AI engagements
- Data analysts and solution engineers scoping production AI systems
- Tech leaders and startup founders building an internal AI capability
- Organisations building or scaling a forward-deployed AI engineering bench
Helpful background (not mandatory)
- Working proficiency in Python or one general-purpose language
- Basic familiarity with REST APIs and version control
- Comfort with cloud concepts — depth is built during the programme
- No prior LLM, RAG, or agent experience required
- Curiosity about solving messy, real business problems with AI
◆ Programme Formats
Delivered the way your organisation needs it
Half-Day Workshop
An executive primer or focused deep-dive on a single FDE capability.
Full-Day Bootcamp
Hands-on build day taking a team from concept to a working prototype.
Multi-Week Cohort
The complete FDE programme with capstone and certification.
Corporate Custom
Rebuilt around your stack, archetypes, and target business outcomes.
◆ What L&D Teams Say
Trusted by enterprise learning leaders
AI Practice · IT Services Firm
Enterprise AI Delivery
Global Systems Integrator
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◆ FAQ
Questions L&D teams ask before signing
An FDE works directly with a customer or internal business unit to design, build, deploy, and operationalise production-grade AI systems — owning the outcome from the first discovery conversation through to adoption, not just the prototype.
This page is for corporate engagements — cohorts of 15–25 engineers from a single organisation. We tailor the curriculum, labs, and capstone to your stack and outcomes. For individual or small-group coaching, contact us and we’ll point you to the right format.
No prior LLM, RAG, or agent experience is required. Working proficiency in Python (or an equivalent language) and basic familiarity with APIs and version control is enough — we build the AI depth during the programme. We can also calibrate depth up or down after a quick skills assessment.
Generic courses teach concepts in a sandbox. We rebuild every engagement around your tech stack, your data, and your business problems — and your team ships a real capstone evaluated by a panel, optionally including your own assessors.
Yes. Module depth, sequencing, lab environments (Azure, AWS, or GCP), frameworks, and the capstone scenario are all adapted per engagement. A 30-minute scoping call is all we need to design your version.
Pricing depends on format, cohort size, duration, and degree of customisation. After a short scoping call we send a clear commercial proposal. Standard engagements follow a day-rate model with a 50% advance on confirmation; international travel and accommodation are reimbursed at actuals.
◆ The Future Belongs To AI Implementers
Anyone can access AI tools. Very few can deploy them inside an organisation.
◆ Skillopedia
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Programme
- Why FDE
- Curriculum
- Delivery Framework
- Who It's For
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