Search⌘ K
AI Features
Home
Courses
Forward Deployed Engineer for AI Systems

Are You Ready for This Course?

Explore the essential foundation for forward deployed engineers moving AI systems into real-world customer settings. This lesson helps you understand how to scope projects effectively, deliver proof of concept solutions under operational constraints, communicate clearly with stakeholders, and manage handoffs. You'll gain practical judgment skills to apply your AI engineering knowledge in dynamic environments where success depends on stakeholder trust and timely delivery.

We'll cover the following...

This course is for engineers who already build AI systems. It focuses on what changes when that work moves into a customer environment: scoping the right problem with the customer, delivering under operational constraints, earning stakeholder trust through reliable delivery, and shipping a solution that remains usable after handoff. Technical knowledge is the starting point. The course develops the judgment needed to apply that knowledge in environments the engineer cannot fully control.

This lesson maps the foundation the course assumes and points to the right resources if any area needs strengthening before we continue.

What this course focuses on

The FDE operating model has six components. The course covers each one in depth.

  • Customer discovery: This is the process of finding the real problem under the stated request. Customers rarely describe their actual constraint in the first conversation. They describe a symptom, a wish, or a solution they already have in mind. The FDE’s job in discovery is to ask the right questions, read the workflow directly, and identify where AI can deliver measurable value given what the organization actually has and what they are actually willing to change.

  • Project scoping: This phase is translating what we learned in discovery into a bounded, deliverable system. A good scope document defines the problem, the success criterion, the data sources, and the explicit boundaries of what will and will not be built. Scoping protects the FDE and the customer from misaligned expectations.

  • Proof of concept (POC) delivery: This phase is building and shipping a working system under real constraints: imperfect data, incomplete access, an external timeline, and stakeholders watching the result. The goal of a POC is to prove or disprove a specific hypothesis, not to build a finished product. Speed and measurability matter more than architectural elegance. A POC that answers the core question in ten days is more valuable than a better-engineered system that answers it in six weeks. The FDE’s technical depth is what makes it possible to build fast without cutting corners. Knowing which shortcuts are acceptable and which create problems downstream is an applied skill, not a theoretical one.

  • Stakeholder communication: This is presenting technical work in terms that allow non-technical stakeholders to make decisions. This includes written updates, live demos, risk conversations, and the final readout. An FDE who can only communicate with engineers leaves value on the table in every customer project.

  • Project handoff: This is leaving behind a system and documentation that another team can own, maintain, and extend. FDEs do not stay in a customer environment indefinitely. A clean handoff is part of the deliverable.

  • Field feedback: This is converting observations from customer projects into product intelligence. FDEs see patterns across organizations that no internal product team can observe from inside the company. A retrieval failure that appears specific to one customer’s data structure often turns out to be a general gap in how a tool handles a certain document type. A stakeholder concern that comes up in one industry shows up again in a different form in another. Capturing those patterns and communicating them internally is part of how FDE work compounds over time.

The course uses AI engineering tools, such as RAG pipelines, agent frameworks, and evaluation suites, as tools for making and validating these decisions. The course centers on a different starting question. In product engineering, the first question is often, “Can we build this?” In forward-deployed work, the question becomes, “What should we build, who is it for, when does it need to work, and how will we prove it delivered value?” This approach assumes the learner already has a working AI engineering foundation.

Your AI engineering foundation

This course assumes practical fluency across the core skill areas, meaning you have built, run, and debugged AI systems in code: written RAG pipelines, designed agent workflows, measured model output quality systematically, and deployed AI applications in production or near-production environments. If any of the areas below need strengthening, the sub-bullets point to the relevant course.

  • LLMs and APIs: Call model APIs from OpenAI, Anthropic, and Gemini in code, handle streaming responses, manage context windows, and reason about token costs and latency. If this area needs strengthening, the following course covers it:

    • Building with OpenAI: This course covers working with the OpenAI API end to end, from basic completions to function calling and advanced API patterns.

  • Prompt engineering: Write effective system prompts and few-shot examples, get structured output reliably from a model, and adapt prompts based on observed failures. To build or refresh this foundation, explore the following:

    • Prompt Engineering Guide: This course covers structured prompting techniques, chain-of-thought reasoning, and prompt evaluation methods.

  • RAG: Build a retrieval-augmented generation (RAG) pipeline end to end, covering embedding, chunking, vector search, and reranking, and diagnose failure modes under real data conditions. If either of these areas is unfamiliar, the following courses go deep on them:

    • RAG with LLMs: This course covers building retrieval pipelines from scratch including indexing, querying, and output grounding.

    • Vector Database: This course covers vector storage, similarity search, and embedding-based retrieval in depth.

  • Agentic frameworks: Build single agent and multi-agents with tool use, understand multi-step orchestration, and have debugged agent failures in code. If practice with any of these frameworks is limited, explore the relevant course below:

  • Building agentic systems: Architect, evaluate, and deploy multi-agent AI systems in production contexts, and understand how system-level design choices affect reliability and controllability. The following courses cover both the architecture and the patterns behind production agentic systems:

    • Agentic AI Systems: This course covers end-to-end design and deployment of production agentic systems.

    • Agentic Design Patterns: This course covers common architectural patterns for reliable and scalable agentic workflows.

  • LLM evaluation: Measure model output quality with a repeatable approach, build a systematic eval suite, and understand where automated evals fall short. If systematic evaluation is an area to develop, this course covers it directly:

    • LLM Evaluation: This covers evaluation frameworks, benchmark design, and automated testing methods for LLM outputs.

  • LLMOps: Understand deployment, latency management, cost tracking, and basic observability for AI systems running in production. For a thorough grounding in operating LLM systems at scale, explore the following:

    • LLMOps: This covers the operational side of running LLM applications at scale, including monitoring, versioning, and cost management.

With the technical foundation mapped, the next question is what changes when those skills move into a real customer project.

How the environment changes the work

Working on an internal or personal AI project gives full control: the data is familiar, access is immediate, the timeline is internal, and success is defined by whoever is building the system. Customer environments add a different layer of conditions on top of the same technical work. What changes between building for an internal team and building for a customer is not the technical surface. It is the judgment layer on top of it: what to scope, what to simplify, what to flag early, and how to keep a stakeholder confident when the environment turns out to be more complicated than it first appeared.

The timeline dimension deserves specific attention. In a customer project, the timeline belongs to the customer, not the engineer. Missing a demo date does not move a deadline out; it damages trust. FDEs develop an instinct for scoping to the time available rather than scoping to the ideal solution. The first deliverable is rarely the complete vision. It is the smallest working system that demonstrates the core value and earns the right to build more.

The AI engineering foundation is the prerequisite. What the rest of this course builds is the judgment to use it effectively in customer environments, where the conditions are set by someone else and the outcome belongs to the people depending on it.