New Program Individual

FDE — Forward Deployed Engineer Program

▊ FRAME · INTEGRATE · ENGINEER · LAUNCH · DRIVE ADOPTION

Learn to deliver AI solutions for a real business problem.

A 10-week, hands-on program. You take one realistic business case from the first customer conversation to the final demo: scoping it, connecting the data, building and testing the AI solution, deploying it and handing it over. Python, SQL and math basics are taught inside the program, and you work with AI coding assistants throughout.

  • 10 Weeks
  • Live · Online
  • Hands-On Labs
  • One Business Case, Start to Finish
  • Python, SQL & Math Basics Included
Book a Free Clarity Call See the Journey

Want to understand how AI and machine learning work first? See B.U.I.L.D. →

What You'll Do

Understand the problem. Build the solution. Hand it over.

A forward deployed engineer works directly with customers to turn a business problem into a working solution. This program follows that work, one stage at a time.

01

Frame it

Understand the customer's problem, agree what success looks like, and write a simple business case.

02

Build it

Connect messy data, then design and test an AI solution: prompts, retrieval, tools and agents.

03

Launch it

Prototype quickly, deploy it, and cover the security and privacy basics.

04

Hand it over

Demo to stakeholders, help people adopt it, and write the documentation that lets others take it on.

Python, SQL and math basics are taught inside the program, at the level you need to work.

01 — Who This Is For

For people who want to deliver AI, not just talk about it.

Engineers, data and analytics professionals, technical consultants, solutions and sales engineers, and motivated career-switchers with basic technical comfort.

Tick anything that sounds like you.

Book a Free Clarity Call

This is not for you if:

  • You want a deep course in machine learning theory - B.U.I.L.D. is the right door.
  • You are looking for a guaranteed job or role. This program teaches skills and builds a project; it does not promise placement.
  • You cannot commit time to hands-on work every week.

02 — How It Works

One business case. Built week by week.

There is no separate capstone. The capstone is the case you build, one piece at a time.

Thread 1

One case, start to finish

Pick a realistic business case. Each week adds one more piece of the same solution, from the first scope to the final demo. Case data is synthetic or public, so there are no confidentiality worries.

Thread 2

AI-assisted engineering

Work in VS Code with an AI coding assistant such as Claude Code, or Cline or Kilo Code. Learn MCP, and where vibe coding works and where it does not. The rule: you can generate it fast, but you still have to understand it, test it and explain it.

Thread 3

Communicate as you go

Short weekly readouts: what we did, what we found, what is next. By the end you will have practised the customer conversation many times.

03 — The Journey

Frame. Integrate. Engineer. Launch. Drive adoption.

Topics follow this order. The pace flexes with the group.

Frame

The FDE role, the engagement lifecycle and your toolkit

  • What a forward deployed engineer does, and how it differs from a software engineer, a solutions architect and a consultant
  • The engagement lifecycle, from the first conversation to handoff
  • Working with ambiguity, and taking ownership
  • Python basics: variables, data types, control flow and functions

Hands-on lab: Set up VS Code, Git and an AI coding assistant, write your first Python scripts, and read the case brief.

Case milestone Choose your business case and write down what you think the problem is, and for whom.

Frame

Python and SQL working basics

  • Python: lists, dictionaries, files, JSON and error handling
  • Calling REST APIs from Python, and basic data handling with pandas
  • SQL basics: SELECT, WHERE, GROUP BY and joins
  • Git and command-line basics

Hands-on lab: Pull data from a sample API with Python, load it into a table and answer business questions with SQL.

Case milestone Explore your case dataset: what is there, what is missing, what looks wrong.

Frame

Math basics for AI, discovery and the business case

  • Math, just enough: statistics and probability intuition, vectors and similarity (so embeddings make sense), accuracy, precision and recall
  • Discovery: stakeholder mapping, interviews, symptoms versus root causes
  • Problem statements, success measures and acceptance criteria
  • A simple business case: value, cost and risk

Hands-on lab: Run a mock discovery call and write a one-page scope with success measures.

Case milestone A one-page scope and a simple business case for your case.

Integrate

Data integration and pipelines

  • Ingesting data from files, databases and APIs
  • ETL and ELT, schema mapping and data quality checks
  • Working within access and permission limits
  • Documenting data lineage and assumptions

Hands-on lab: Combine three data sources into one clean, queryable dataset.

Case milestone A working data layer for your case, with a short note on sources, assumptions and gaps.

Engineer

LLM solutions I: prompts, embeddings and retrieval (RAG)

  • Prompt design and structured outputs
  • Embeddings and vector search
  • Retrieval-augmented generation over documents, with sources

Hands-on lab: Build a first assistant over the case documents that answers with its sources.

Case milestone The first working AI component for your case.

Engineer

LLM solutions II: tools, agents and MCP

  • Tool use and function calling
  • Simple agent workflows with a human approval step
  • MCP in plain terms: how AI connects to your tools and data
  • Choosing between prompting, retrieval and fine-tuning (awareness only)
  • Text, image and audio inputs, where the case needs them

Hands-on lab: Add one tool or workflow step that stops for human approval.

Case milestone Your solution handles a realistic end-to-end scenario.

Engineer

Evaluation, reliability and guardrails

  • Building evaluation sets and choosing quality measures
  • Hallucinations and other failure modes
  • Guardrails and human-in-the-loop design
  • Managing cost and latency
  • Testing and reviewing AI-written code

Hands-on lab: Build an evaluation harness and improve your solution against a target.

Case milestone Evaluation results against the success measures you set earlier.

Launch

Rapid prototyping and deployment

  • Time-boxed prototyping, and vibe coding: where it works and where it breaks
  • A simple user-facing front end
  • Docker and basic CI/CD
  • Configuration and secrets, and deploying a prototype

Hands-on lab: Ship a deployed prototype with a user interface in a time-boxed sprint.

Case milestone A deployed prototype that a tester can use.

Launch

Security, privacy and stakeholders

  • Authentication, access control and protecting secrets
  • Personal data and privacy rules, including India’s DPDP Act
  • What not to paste into AI tools
  • Executive storytelling and live demos; managing scope and expectations; training end users

Hands-on lab: Complete a security and privacy checklist and fix the gaps, then rehearse a short executive demo.

Case milestone Security checklist completed and a demo script ready.

Drive adoption

Handoff, productizing and the final demo

  • Runbooks, documentation and handoff plans
  • Spotting what is reusable
  • Measuring outcomes and planning the next phase

Hands-on lab: Package your solution as a reusable template with documentation, deliver the final demo and answer stakeholder questions.

Case milestone Final demo and handoff pack.

04 — What You Take Away

A working project. Not just exercises.

What you carry out of the program.

01

A deployed solution for a business case, built piece by piece over the program

02

A one-page scope and business case

03

Evaluation results against agreed success measures

04

A security and privacy checklist for your solution

05

Documentation and a reusable template

06

Hands-on practice with AI coding assistants, MCP and deployment tools

05 — Tools

Tools you will use. Examples, adapted per batch.

Core

The basics

Python, SQL, Git, VS Code and the command line.

AI-assisted

Working with AI

Claude Code; VS Code with Cline or Kilo Code; MCP.

AI solutions

Building the solution

LLM APIs, embeddings and a vector store, retrieval, tool use and agents.

Launch

Prototype and deploy

A simple front end, Docker, basic CI/CD and one simple hosting option.

TOOLS CHANGE QUICKLY · THE PROGRAM TEACHES THE METHOD AND USES THESE AS CURRENT EXAMPLES

06 — Format

Live, hands-on, built around one project.

Duration

10 weeks

Live online sessions with hands-on labs.

Method

Build, don't just watch

Every week includes a hands-on lab and a milestone on your case.

Foundations

Basics included

Python, SQL and math basics are taught in the first weeks.

Next step

Start with a free call

Check the fit with GK before you commit.

07 — Straight answers

Questions before you step in.

Do I need to know Python, SQL or maths already?

No. Python, SQL and math basics are taught inside the program, in the first weeks, at the level you need to work. Some technical comfort helps.

Is this the same as B.U.I.L.D.?

No. B.U.I.L.D. teaches how AI and machine learning work, from statistics to deep learning and generative AI. This program teaches how to deliver an AI solution for a business problem: scoping, data, building, testing, deployment and handoff. The two overlap on Python, SQL and generative AI, and you can take either one on its own.

Will this get me a job as a forward deployed engineer?

No program can promise that. You leave with applied skills and a working project you can show and explain. What you do with it is up to you.

What business case will I work on?

A realistic case from a short list, using synthetic or public data, so you never need confidential company data. The case runs through the whole program, and each week adds one more piece of it.

Which AI tools will we use?

AI coding assistants such as Claude Code, or Cline or Kilo Code inside VS Code, plus current LLM tools. Tools change quickly, so they may be adapted to each batch. The method stays the same.

Is this vibe coding?

Vibe coding is covered, including where it works and where it breaks. The rule is simple: you can generate code fast, but you still have to understand it, test it and be able to explain it.

Contact

Ready to deliver AI
for a real business problem?

Start with a free 30-minute clarity call.

Book a Free Clarity Call

Want the foundations in AI first? See B.U.I.L.D. →

info@24x7coach.com+91 95059 44455
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