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Corporate Training Programme

Forward Deployed Engineer (AI)
capability building,
designed for your organisation.

A custom-built corporate programme for Senior developers, tech leads, and solution engineers (5+ years) deployed into client accounts to scope, build, and own AI solutions end-to-end. We design the curriculum around your tech stack, project archetypes, and target business outcomes — delivered by domain-expert trainers and reinforced through AI-evaluated assessments.

Duration80 hours · 4 weeks
Format75% virtual instructor-led + 25% in-person workshop days · cohort 15-25
CohortFrom 15 learners · max 25
★★★★★4.9 on Google · 9,000+ professionals trainedEnterprise-ready AI productivity programme
Programmes delivered for →
CGIDXC TechnologyCapgeminiUSTMassMutualTata ConsultancyWiproAccentureHCLInfosysCGIDXC TechnologyCapgeminiUSTMassMutualTata ConsultancyWiproAccentureHCLInfosys
Curriculum & syllabus

A modular syllabus, built to be tailored.

Below is our reference curriculum. Every syllabus we deliver is tailored to your customer-specific requirements — module depth, sequencing, lab environments, and capstone projects are adapted to your team's starting point, tech stack, and target outcomes.

This is a reference structure, not a fixed catalogue.We rebuild the syllabus per engagement. Tell us your context, and we'll send a customised version within 1 business day.
Get Customised Syllabus
Ground the FDE in the modern LLM stack and the seat itself. What separates a forward-deployed engineer from a senior dev or solution architect.
  • What an FDE actually does: discovery to adoption ownership across the full lifecycle
  • Case study: teardown of 3 real FDE engagements — wins and failures — across BFSI, retail, and manufacturing
  • The 2026 AI solution landscape: where RAG dominates, where agents earn their keep, where classical ML still wins
  • Lab: map your own client portfolio to FDE engagement archetypes

Want the full module-by-module syllabus, sample assignments, and pricing?

One PDF — sent to your inbox in under a minute.

Learning objectives & outcomes

Demonstrable skills your team will apply on live projects.

01 / Capability

Run a discovery conversation that produces a fundable solution sketch

Workshop facilitation, problem framing, JTBD mapping, AI-pattern selection, architecture diagramming — within 5 days of first client contact.

02 / Capability

Build production-grade RAG and agentic services on customer cloud

Chunking strategy selection, vector store choice, hybrid retrieval, re-ranking, multi-agent orchestration via LangGraph/CrewAI/AutoGen on Azure or AWS.

03 / Capability

Operationalise AI services with observability, guardrails, and cost controls

OpenTelemetry traces, online evaluation, NeMo/Guardrails AI, semantic caching, model routing, fine-tuning when warranted.

04 / Outcome

Pass the joint NovelVista + client capstone panel

Each FDE delivers: discovery doc, solution architecture, deployed service, monitoring dashboard, red-team report, and 90-day adoption plan.

05 / Outcome

Earn the FDE certification credential

Cohort first-attempt completion rate of 89% across enterprise deployments. Two attempts permitted. Co-branded certificates available for ATP partner clients.

06 / Outcome

Become the AI champion in your client accounts

FDE alumni typically take a 2-3 grade leap in client-facing scope within 6 months and become the lead solution voice on AI deals.

Skills transformation

Where your team is now vs where they'll be after the programme.

Before · Day Zero

Where most teams start

  • ·Senior developer or tech lead, but new to the FDE seat — unsure how to scope an AI engagement from a discovery conversation
  • ·Comfortable with Python or one general-purpose language but no production LLM/RAG experience
  • ·Cloud fluency (Azure preferred, AWS/GCP acceptable) but not yet shipped agentic systems on customer infrastructure
  • ·No structured methodology for problem framing, jobs-to-be-done, or AI-pattern selection in a client setting
  • ·Limited fluency with multi-agent orchestration frameworks (LangGraph, CrewAI, AutoGen) for real client workloads
  • ·Cannot independently take a prototype to a production-grade, observable, cost-aware service with monitoring and red-team coverage
After · Programme Close

Where they'll arrive

  • Client discovery & solutioning — runs discovery workshops, frames problems, scopes MVPs, maps business needs to AI patterns
  • Agentic AI & orchestration — designs and builds multi-agent systems with LangGraph, CrewAI, and AutoGen on customer cloud
  • End-to-end build & deployment — takes a solution from prototype to production-grade FastAPI service on Azure or AWS
  • Operationalisation & adoption — instruments, monitors, red-teams, and drives user adoption post-deployment
  • Capstone-evidenced delivery — ships a complete client-scenario solution evaluated by a joint NovelVista + client-side panel
  • FDE certification — credential portfolio piece for client-facing AI engineering roles at services firms
Why NovelVista

Built for L&D outcomes, not seat counts.

16–20
Hours of blended learning across VILT and self-paced labs
13
Modules covering prompting, Custom GPTs, automation, multimodal AI, and responsible use
40–60%
Target reduction in recurring task effort through documented workflow compression
50+
Tested, role-specific prompts learners leave with in their personal prompt library

Prompt discipline, not prompt luck

Learners move from trial-and-error prompting to named patterns such as role prompting, few-shot, prompt chaining, and self-critique.

Reusable team assets

The programme produces Custom GPTs, reusable workflow templates, and a shared prompt library that teams can govern and scale.

Daily productivity workflows

Labs focus on email, reports, slides, meetings, spreadsheets, research synthesis, and role-based business assignments.

$

Measured time savings

Capstone workflows document recurring task compression, review-cycle reduction, and before/after productivity improvements.

Responsible enterprise use

Learners practise confidentiality, IP, bias detection, verification checklists, and safe-use protocols before adoption at scale.

Sustainment built in

30-day, 60-day, and 90-day check-ins help learners keep pace as ChatGPT features and frontier models evolve.

Delivery framework

A four-milestone path from skill gap to client-ready.

1
Milestone One

Foundation & baseline

Establish a working mental model of ChatGPT, frontier models, tokens, context windows, hallucination risks, and model-selection trade-offs.

2
Milestone Two

Prompt engineering labs

Learners practise CRISPE, SPEAR, role prompting, constraint-led prompting, few-shot prompting, self-critique, and prompt iteration on real work scenarios.

3
Milestone Three

Custom GPTs & workflow automation

Each learner builds reusable GPTs and connects ChatGPT to productivity tools for email, documents, spreadsheets, meetings, and research workflows.

4
Milestone Four

Capstone & sustainment

Learners demonstrate a personal AI productivity system and continue with prompt-of-the-week, model-of-the-month, and 30/60/90-day check-ins.

Want this curriculum aligned to your tech stack and project archetypes?

Corporate vs Individual

Why enterprise teams choose the B2B engagement model.

Feature / Benefit
Individual (B2C)
Enterprise (B2B)RECOMMENDED
Structured prompt engineering methodology
— Ad-hoc prompt tips
Named patterns and team standards
Custom GPTs for team reuse
— Individual experimentation
Role-specific GPTs with guardrails
Productivity workflow automation
— Basic tool usage
Email, spreadsheets, calendars, documents, and meetings
Safe-use and data-handling protocol
— General awareness
Enterprise policy-ready protocol
Capstone with measurable time savings
— Course completion only
Documented 40–60% recurring task compression
Shared prompt library
— Personal notes
50+ tested prompts and cohort repository
Role-track workshops
— Generic examples
Marketing, Sales, HR, Operations, and PM tracks
Post-programme sustainment cadence
— Limited follow-up
30-day, 60-day, and 90-day check-ins
Lead Trainer

Domain-expert trainers, not professional presenters.

"My job isn't to teach ChatGPT as a tool — it's to help professionals build repeatable AI workflows, verify the output, and reclaim hours from routine work."

AM
Akshad Modi
ChatGPT Workflow Mentor · Prompt Engineering · Productivity Automation
Faculty

Taught by people who've actually shipped the work.

Prompt-engineering depth across Chain-of-Thought, role prompting, few-shot learning, prompt chaining, and self-critique.
Workflow-first delivery covering email, reports, slides, meetings, spreadsheets, research, and role-based business assignments.
Enterprise-safe adoption with confidentiality, IP, bias, verification, and data-handling guardrails built into every lab.
Capstone accountability where each learner demonstrates a reusable AI productivity system with documented time savings.
Audience & eligibility

Built for L&D leaders and their learners.

Who this is for

  • ·Knowledge workers who want to apply ChatGPT productively in their daily workflows
  • ·Business analysts, consultants, marketing professionals, project managers, and individual contributors
  • ·Teams that use ChatGPT for occasional drafting but need reliable, business-grade outputs
  • ·Managers looking to establish team-wide prompt standards and safe-use protocols
  • ·Organisations that want to automate repetitive work across email, spreadsheets, calendars, and documents

Pre-requisites

  • ·No coding prerequisite for business and productivity tracks
  • ·Basic familiarity with workplace tools such as email, documents, spreadsheets, slides, and meetings
  • ·Willingness to bring real recurring tasks into labs for workflow redesign
  • ·Enterprise cohorts should align data-handling expectations before learners use company or client information
What L&D teams say

Trusted by L&D leaders across the world.

★★★★★

"The programme moved our team from random prompting to a repeatable method. The prompt library and Custom GPTs became assets we could actually reuse."

LD
L&D Leader
Capability Development
★★★★★

"The most useful part was workflow automation. Learners took their weekly reports, meeting recaps, and research tasks and reduced hours of repetitive effort."

PM
Programme Manager
Enterprise Operations
★★★★★

"Responsible use was handled practically. The team finally understood what can be pasted, what must be masked, and how to verify output before sending it."

CO
Compliance Owner
Business Governance
Frequently asked

Questions L&D teams ask before signing.

A Forward Deployed Engineer works directly with enterprise customers to design, deploy, customize, and operationalize production-grade AI systems and agent workflows.

Let's get specific

A 30-minute scoping call is all we need to design your programme.

Phone1800 212 2003Emailtraining@novelvista.comHoursMon – Sat, 9:00 to 19:00 IST
Forward Deployed Engineer AI Training 2026 | NovelVista