Description:

Modern enterprises require engineers who can do more than write code—they need professionals who can understand customer challenges, architect AI-powered solutions, navigate enterprise environments, and deliver measurable business outcomes. The Forward Deployed Engineer (FDE) Bootcamp is an intensive, hands-on program designed for mid-to-senior software engineers, architects, QA professionals, and technical leaders who want to bridge the gap between software engineering, customer engagement, enterprise architecture, and AI solution delivery. Participants develop the consulting, communication, and technical leadership skills needed to successfully engage stakeholders, translate business requirements into technical solutions, and drive customer success.

The bootcamp strengthens core engineering skills through an accelerated refresher on Python, Git, SQL, Google Cloud Platform (GCP), Docker, and Kubernetes, followed by enterprise data engineering concepts including ETL/ELT pipelines, legacy system integration, BigQuery, MongoDB, and Apache Spark. Participants learn how to work with complex enterprise data environments while building scalable, production-ready solutions that integrate seamlessly with existing business systems.

Participants then explore modern AI engineering, covering Artificial Intelligence, Machine Learning, Large Language Models (LLMs), advanced prompt engineering, Retrieval-Augmented Generation (RAG), Vector Databases such as Pinecone, Weaviate, and PGVector, and the design of intelligent agentic applications using LangChain and LangGraph. The program also focuses on production deployment through AI DevOps (LLMOps), CI/CD pipelines, observability, monitoring, security, IAM, prompt injection mitigation, HIPAA and PII compliance, and governance using the NIST AI Risk Management Framework (AI RMF) to ensure AI solutions are secure, reliable, and enterprise ready.

Throughout the bootcamp, participants reinforce their learning through hands-on labs, customer discovery workshops, stakeholder interview simulations, architecture exercises, and real-world implementation projects. The program culminates in a comprehensive capstone project in which teams design, build, deploy, monitor, and present an enterprise AI solution that addresses a realistic customer use case. Students will leave the program equipped with the technical expertise, consulting mindset, and practical experience required to confidently serve as Forward Deployed Engineers and lead successful enterprise AI initiatives from discovery through production deployment.

Duration: 

4 Weeks

Course Code: BDT635

This bootcamp is specifically designed for:

1. Mid-to-senior software engineers, architects, QA professionals, managers/senior managers
2. The emphasis is on developing engineers who can bridge customer problems, AI engineering, enterprise architecture, and business outcomes, rather than simply building software

Participants are already proficient in core software engineering (a programming language, Git, basic SQL, basic cloud/container concepts)

Course Outline:

Foundations of Forward Deployed Engineering & Customer Discovery

The FDE Blueprint & Customer-Centric Engineering

1. Core Concepts: Anatomy of the FDE role; shifting from isolated SWE sprint cycles to iterative client delivery loops; navigating the friction between scalable product roadmaps and bespoke client requirements
2. Comparative Dynamics: Structural differences, boundaries, and collaboration models between SWE, Solutions Engineering (SE), Product Management (PM), and FDEs
3. Key Capabilities for Customer-Facing Engineers: End-to-end customer engagement lifecycle, Balancing technical excellence with business outcomes, Managing ambiguity in enterprise environments, Real-world FDE case studies

Hands-on:

1. Analyze multiple customer implementation scenarios
2. Map business challenges to technical solutions
3. Evaluate successful and unsuccessful deployment examples
4. Create a stakeholder value analysis

Discovery, Requirements Gathering & Stakeholder Mapping

1. Discovery & Requirements Elicitation: discovery methodologies, business process analysis, identifying hidden requirements, translating business needs into technical specifications
2. Stakeholder Mapping & Interviewing: identifying stakeholders, conducting technical and non‑technical interviews, managing conflicting priorities
3. Defining Success & Validation Criteria: establishing measurable success metrics and ensuring alignment across stakeholders
4. Hands-on: Live role-play mock customer interviews simulating defensive IT administrators and non-technical business executives

Communication, Consulting & Customer Engagement

1. Executive & Technical Communication: fundamentals, technical storytelling, delivering difficult recommendations
2. Customer Meeting & Engagement Skills: running customer meetings, expectation management, escalation handling, building stakeholder trust
3. Consulting & Advisory Practices: status reporting, framing recommendations, guiding customers through complex decisions
4. Hands-on: Executive briefing simulations, customer presentation practice, technical proposal review

Software Engineering & Enterprise Data Fundamentals

Core Engineering & Cloud Refresher (accelerated - assumes prior proficiency)

1. Python: for rapid prototyping and integration glue code
2. Git collaboration at scale (branching strategy, PR review, rebasing, monorepo basics)
3. SQL refresher (joins, window functions, subqueries, query optimization)
4. Cloud platform refresher (GCP-focused: compute, storage, networking, IAM/security fundamentals)
5. Containers refresher: Docker (Dockerfile best practices, build/run/push)
6. Kubernetes fundamentals (pods, deployments, services, config, scaling basics)
7. Hands-on: Multiple labs aligned to each topic (Docker, GCP services, IAM, networking, Kubernetes) to reinforce practical skills through incremental, guided exercises

Data Fundamentals for FDE Work

1. Data pipelines basics (ETL/ELT) for integrating customer data sources
2. Designing for legacy and constraint-heavy environments (limited/undocumented APIs, brittle schemas, complex or siloed data)
3. Working with semi-structured/structured enterprise data at scale (BigQuery)
4. NoSQL touchpoints (MongoDB) for unstructured/document-style sources
5. Big Data Tools & Ecosystems: Apache Spark, basic distributed processing

Hands-on: Multiple labs covering ETL/ELT patterns, legacy system analysis, schema exploration, BigQuery operations, and MongoDB ingestion workflows, each reinforcing practical integration skills through guided exercise

Modern AI/ML Specification & Agentic AI Solutions

Operational AI/ML Concepts

1. AI/ML Essentials: Core concepts of AI, ML, and Deep Learning; what models are, what they learn, and key learning paradigms (supervised, unsupervised, reinforcement)
2. Enterprise Applications: How AI/ML powers automation, forecasting, anomaly detection, personalization, and agentic workflows in real enterprise environments
3. Hands‑on Lab: Review an enterprise process and draft a concise AI/ML or agent specification outlining inputs, outputs, constraints, and integration points

LLM & Agent Fundamentals

1. LLM fundamentals: Transformer architecture, tokenomics, context window management, and advanced prompting mechanics (Chain-of-Thought, ReAct)
2. Architectural Trade-offs: Fine-tuning basics vs. Prompt Engineering vs. RAG (Cost-benefit analysis for enterprise choice)
3. Vector Databases in Production: High-availability deployment of Pinecone, Weaviate, or PGVector)
4. Advanced RAG Pipelines End-to-End: Advanced ingestion, chunking strategies (parent-child, semantic), and re-ranking mechanisms
5. Evaluation basics for LLM outputs (relevance, faithfulness, hallucination checks)
6. Hands-on: Build a multi-tool agent (RAG retrieval + at least one external API/tool call) on LangChain/LangGraph

AI DevOps and AI Governance

AI DevOps (LLMOps) & Secure Deployment

1. CI/CD for AI applications (prompt/version pipelines, eval gates before deploying)
2. Prompt injection: attack patterns and mitigation strategies
3. Data privacy and compliance awareness: HIPAA, PII handling, data residency considerations
4. Secrets, IAM, and access control for AI services in enterprise environments
5. Hands-on: Add a CI/CD pipeline with eval gates and a basic prompt-injection test suite

Observability and Telemetry for AI Systems

1. Instrumenting AI applications: Cloud Logging, tracing agent/tool calls
2. Performance monitoring (latency, token usage/cost, error rates)
3. LLM-specific observability: drift detection, output quality monitoring, guardrail triggers
4. MLOps basics: model/prompt versioning and monitoring in production
5. Hands-on: Instrument the agent with logging, tracing, and a basic monitoring dashboard

AI Risk Management and Governance

1. Introduction to the NIST AI Risk Management Framework (AI RMF): purpose, scope, and how it fits alongside HIPAA/PII compliance work from the prior module
2. The four core functions: Govern, Map, Measure, Manage:
   1. Govern: establishing accountability, policies, and organizational risk culture for AI systems
   2. Map: identifying context, intended use, and risks specific to the deployed agent/RAG solution
   3. Measure: assessing and tracking risks (bias, robustness, security, transparency) using qualitative/quantitative methods
   4. Manage: prioritizing and responding to identified risks, including residual risk and incident response
   5. Mapping AI RMF functions to enterprise deployment artifacts FDEs already produce (security/compliance checklist, eval gates, observability dashboards)
   6. Applying AI RMF in customer-facing engagements: using it as a shared vocabulary with customer risk, legal, and compliance stakeholders
   7. Common gaps in fast-moving FDE pilots (undocumented risk decisions, missing accountability owners, lack of measurable risk criteria) and how to avoid them
   8. Hands-on: Conduct a NIST AI RMF risk assessment on the Week 3 agent (Govern/Map/Measure/Manage walkthrough)

Capstone Project & Use Case

1. Project Overview
2. Complete projects to get experience and practice
3. Presentation of project findings and Insights
4. Industry Use Case Studies

Certification (Optional)

1. Certification Overview
2. Identify the right certification for you
3. Tips to prepare for certification