What does the curriculum cover?

Ten modules across two tracks. Enterprise Data Engineering and Security covers AI-ready ingestion and chunking, embeddings and vectorization, vector databases and search, backend infrastructure and cloud deployment, and pipeline quality, observability and data privacy. Resilient AI Engineering and Security Guardrails covers LLM architecture, prompt engineering and AI security, RAG foundations and data access control, tool calling and micro-agents, fault tolerance and idempotency, and agent governance, tracing and final deployment.

The FDE Accelerator | Forward Deployed Engineer Bootcamp

Q: How is each day structured?

Every day follows the same four steps. A real client scenario sets the business context, the underlying concept is taught in 45 minutes before any code is written, engineers build the mechanic themselves in raw Python, and then rebuild the same problem with the production tooling teams actually use in the field.

01 The Gap

Five roles, none that ships the whole thing

Every AI project splits across the same five people. Each owns a real piece — and each stops short of a system running in production on the client's stack.

Data Scientist

Owns the model — features, embeddings, evaluation.

Stops at the notebook; not accountable for deployment or uptime.

AI Engineer

Owns the LLM layer — prompts, RAG, tool calling.

Thin on data pipelines and cloud infrastructure.

Software Engineer

Owns the system — APIs, services, deployment.

Treats AI as a black box; unready for PII and prompt-injection risk.

Domain Expert with Claude

Owns the business logic and knows what “correct” looks like.

Assumes anything ships with an AI assistant alone — underrates data, security and scale.

Business Analyst

Owns the requirements — frames the client problem clearly.

Can specify the solution but can't build, secure or deploy it.

// the_missing_role

Forward Deployed Engineer

Spans all five — modeling intuition, production AI systems, engineering discipline and business judgment — and is accountable for the whole thing on the client's stack. That role is what the Accelerator builds.

04 Curriculum

Ten modules, two tracks

Ten days at 3.5 hours a day, or four weekends across Saturday and Sunday. The first track makes the data trustworthy; the second makes the AI layer survive contact with production. Open any module to see its client scenario, the from-scratch build, the production tooling and the Assessfy lab.

// track_01 · days 1–5

Enterprise Data Engineering & Security

Scalable, secure, vector-ready data pipelines

Goal: shift from a traditional ETL mindset to building scalable, secure, vector-ready data pipelines.

01AI-Ready Data Ingestion & Chunking Strategies

Client scenarioA retail client needs to extract insights from thousands of unstructured supplier contracts.

Concept 45 min: Why unstructured data breaks traditional ETL, why context must be preserved, and semantic vs. fixed-size vs. recursive chunking.
From scratch 1 hr: A Python script that parses a multi-page PDF, cleans header and footer artifacts, and splits the text into 500-word overlapping chunks.
Tooling 1.5 hrs: LangChain document loaders and text splitters, compared against the scratch output for quality.
Assessfy lab: The parsing script, auto-graded on edge cases such as malformed PDF tables.

02Embeddings & The Math of Vectorization

Client scenarioA CPG brand wants to build a semantic product-matching engine.

Concept 45 min: High-dimensional space, semantic meaning, and the mathematics of cosine similarity.
From scratch 1 hr: Generate vectors with a lightweight local model (sentence-transformers via Hugging Face), then write cosine similarity as a raw NumPy function.
Tooling 1.5 hrs: Integrating a hosted embeddings API, with rate limiting and cost management at scale.
Assessfy lab: The NumPy cosine-similarity function, tested against edge cases such as zero vectors.

03Vector Databases & Search Algorithms

Client scenarioAn e-commerce client wants hybrid semantic search on its storefront.

Concept 45 min: Why SQL fails at similarity search. HNSW (Hierarchical Navigable Small World) indexes and metadata filtering.
From scratch 1 hr: A crude in-memory vector store built on Python dictionaries and the Day 2 similarity function, ranking the top-K documents for a query.
Tooling 1.5 hrs: Deploying and querying a production vector database (Pinecone, ChromaDB or pgvector) with hybrid semantic + metadata search.
Assessfy lab: The in-memory vector store logic.

04Backend Infrastructure & Cloud Deployment Realities

Client scenarioServing the new AI search engine securely to a client's internal frontend.

Concept 45 min: AI needs a robust delivery mechanism: RESTful principles, streaming with Server-Sent Events, and the move from localhost to VPCs, API gateways and strict firewalls.
From scratch 1 hr: A basic API on Python's built-in http.server that serves the Day 3 search results.
Tooling 1.5 hrs: A robust FastAPI service with Pydantic models that strictly validate incoming queries and outgoing AI responses.
Assessfy lab: The FastAPI codebase, checked for response schemas and error-code handling.

05Pipeline Quality, Observability & Data Privacy

Client scenarioA healthcare client requires that no patient health information (PHI) or PII ever reaches a public LLM endpoint.

Concept 45 min: Garbage in, garbage out: data drift, missing chunks, and enterprise data-governance constraints such as PII masking.
From scratch 1 hr: Python assertions and regex pipelines that detect and mask PII (phone numbers, SSNs) before it reaches the database, and catch extreme vector outliers.
Tooling 1.5 hrs: Data observability tools such as Evidently AI, and enterprise PII scrubbing with Microsoft Presidio.
Assessfy lab: Week-one conceptual quiz — multiple choice and short answer.

// track_02 · days 6–10

Resilient AI Engineering & Security Guardrails

Secure, fault-tolerant interactions with LLMs

Goal: move from passive APIs to secure, fault-tolerant interactions with large language models.

06LLM Architecture, Prompt Engineering & AI Security

Client scenarioA customer-support bot was tricked into offering a customer a 99% discount.

Concept 45 min: Transformer basics, zero-shot vs. few-shot prompting, and AI security: prompt injection, jailbreaks and defensive prompting.
From scratch 1 hr: Raw HTTP requests to an LLM endpoint, with system prompts that strictly separate system instructions from untrusted user input.
Tooling 1.5 hrs: The official OpenAI and Anthropic SDKs, using structured outputs to force strict JSON instead of hallucinated formats.
Assessfy lab: The prompt-engineering lab — Assessfy fires prompt-injection attempts at your system prompt and checks JSON schema compliance.

07RAG Foundations & Data Access Control

Client scenarioAn internal HR bot must ensure standard employees cannot query executive compensation documents.

Concept 45 min: Grounding models to prevent hallucination, the end-to-end RAG architecture, and role-based access control (RBAC) in document retrieval.
From scratch 1 hr: A RAG pipeline in plain Python that combines Day 3 vector search with Day 6 raw LLM calls, passing user-role metadata into retrieval.
Tooling 1.5 hrs: A production-grade RAG pipeline in LlamaIndex or LangChain, with built-in retrievers and custom prompt templates.
Assessfy lab: The Python RAG integration script.

08Tool Calling (Function Calling) & Micro-Agents

Client scenarioA supply-chain agent needs to query a live SQL database to check inventory levels.

Concept 45 min: Moving from text generation to models that take action. Single prompts vs. specialized micro-agents.
From scratch 1 hr: A strict prompt that makes the LLM emit a JSON “action” object, parsed in Python to run a local function (check_inventory) and feed the result back by hand.
Tooling 1.5 hrs: Native OpenAI function calling, and an introduction to routing in LangGraph.
Assessfy lab: The manual tool-calling execution loop.

09Fault Tolerance & System Idempotency

Client scenarioAn agent booking logistics shipments fails halfway on a flaky API, and the retry books a duplicate shipment.

Concept 45 min: AI non-determinism in production, API rate limits and backoff strategies, and why idempotency is critical for agents that change state.
From scratch 1 hr: An idempotency-key mechanism built with Python decorators and a simulated cache, so a function never runs twice in a flaky-network retry loop.
Tooling 1.5 hrs: Redis and database constraints to manage distributed state and locks in agent workflows.
Assessfy lab: The idempotency wrapper — Assessfy aggressively runs retry loops to prove the underlying function executes exactly once.

10Agent Governance, Tracing & Final Deployment

Client scenarioHanding a fully functional AI application over to a client's IT Ops team for production management.

Concept 45 min: Why standard logging is not enough for AI: trace graphs, token budgeting and containerization.
From scratch 1 hr: Structured JSON logging of prompts, token counts, latency and tool calls with parent-child span tracking, and the app packaged in a Dockerfile.
Tooling 1.5 hrs: LLM observability platforms such as Langfuse or LangSmith to trace execution graphs, plus secure secrets management for API keys in the cloud.
Assessfy lab: The final two-week conceptual mastery assessment.

// before_day_01

What you'll need

Python 3.10+ on your own machine
Docker — for vector databases and the Day 10 packaging lab
An IDE — VS Code recommended

// production_tooling

What the labs are built with

NumPysentence-transformersLangChainLlamaIndex LangGraphPineconeChromaDBpgvector FastAPIPydanticOpenAI SDKAnthropic SDK Microsoft PresidioEvidently AIRedisDocker LangfuseLangSmith

Where a lab offers alternatives (for example Pinecone, ChromaDB or pgvector), the cohort works with one of them.

06 FAQ

Common questions

What is the FDE Accelerator?

A two-week, project-based bootcamp from Aegis School of Data Science & AI that builds Forward Deployed Engineers: engineers who can ship secure, production-grade AI data systems end to end on a client's stack. Certification is powered by Assessfy.

What is a Forward Deployed Engineer?

A Forward Deployed Engineer spans the five roles that usually split an AI project between them — modeling intuition, production AI systems, engineering discipline and business judgment. Unlike any one of those roles alone, the FDE is accountable for the whole system running on the client's stack.

How long is the program, and how is it scheduled?

Ten days at 3.5 hours per day. It can be taken as two continuous weeks, or spread across four weekends on Saturday and Sunday.

How is each day structured?

Every day follows the same four steps: a real client scenario sets the business context; the underlying concept is taught in 45 minutes before any code is written; engineers build the mechanic themselves in raw Python; then they rebuild the same problem with the production tooling teams actually use in the field.

How is the program assessed?

Each day's lab is submitted to Assessfy and auto-graded immediately, including adversarial edge cases such as prompt injection attempts. The two weeks build toward a working capstone system rather than a quiz, and the program closes with the Forward Deployed AI Engineer Certification — a dual-camera proctored exam of 180 minutes and 43 questions, mixed format, at difficult level.

Who should take this program?

Engineers who are already close to production and want to own an AI system end to end — data scientists who want to get past the notebook, AI engineers who need the data and infrastructure layer, and software engineers who need to stop treating AI as a black box. It is built for practitioners, not beginners.

What do I need to set up before Day 1?

Python 3.10 or later, Docker (used for the vector databases and the Day 10 packaging lab), and an IDE — VS Code is recommended. Every lab is submitted on Assessfy, so you will also need a reliable internet connection.

Build engineers who ship.

Five roles, none that ships the whole thing

Data Scientist

AI Engineer

Software Engineer

Domain Expert with Claude

Business Analyst

Forward Deployed Engineer

India's first, and among its most credible

Taught in the same order, every day

Business Context

Concept

Scratch

Tool

Dr. Vinay Kulkarni

Dr. Ashish Tendulkar

Bhupesh Daheria

Ten modules, two tracks

Enterprise Data Engineering & Security

Resilient AI Engineering & Security Guardrails

What you'll need

What the labs are built with

Every skill verified before it counts

Daily auto-graded labs

Project-based capstone

Dual-camera proctored exam

Forward Deployed AI Engineer Certification Exam

Common questions

Become a Forward Deployed Engineer

Request a callback

Thank you!

Build engineers who ship.