Forward Deployed AI Engineering: From Customer Problem to Production
Learn how a Forward Deployed AI Engineer turns a customer's business problem into a working AI solution. Practice discovery, scoping, software and system integration, evaluation, and deployment through a small end-to-end project. Build a production-oriented prototype and learn the checks needed for a responsible customer handoff.
Level: beginner · 24 lessons · about 8 h · Free · Created by Sandip Das
What you will learn
Translate a customer workflow into requirements, measurable success criteria, and a scoped AI use case.
Build a small AI application using an API, structured outputs, and basic error handling.
Integrate customer-like documents or data through retrieval and an existing-system API.
Evaluate task quality, failure cases, latency, and cost using a small repeatable test set.
Deploy a production-oriented prototype with secrets management, logging, and a rollback plan.
Demonstrate the solution, document limitations, and prepare a customer handoff and iteration plan.
Course outline
1. Discover the Customer Problem and Scope the Solution
Define a measurable, privacy-aware employee-policy support workflow that becomes the course's incremental capstone.
Map the Workflow and the Forward Deployed Engineer's Role(20 min)· free preview Use a guided discovery interview to map a fictional employee's policy question, support-agent review, and ticket handoff; distinguish delivery work from a model demo. [S30][S2]
Agree on Business Value and Success Criteria(20 min) Turn the fictional workflow's baseline into a one-page discovery brief with stakeholder-specific measures and explicitly assumed savings. [S2][S11][S12]
Scope a Thin, Safe End-to-End Prototype(20 min) Compare rules, search, and AI for the same problem; scope a cited policy-answer draft plus an approved synthetic ticket operation, with a local mock path and no paid-service requirement. [S11][S19][S20]
2. Build Just Enough Python and HTTP
Use scaffolded exercises to build the programming and API skills required by the capstone without assuming previous coding experience.
Run a Scaffolded Python Project(20 min) Set up the supplied project and virtual environment, run a script, and change a small input; use diagrams and command explanations for first-time programmers. [S21][S22]
Transform Records with Python Functions(20 min) Learn variables, strings, lists, dictionaries, conditions, and functions through small edits to synthetic support records. [S22]
Read JSON and Call an HTTP API(20 min) Connect to a supplied local mock service while learning URLs, GET and POST, headers, JSON, status codes, timeouts, and exceptions. [S24][S22]
Test the API Boundary with Fakes(20 min) Use a provided test scaffold to isolate service calls, assert request arguments, and replay successful and failed interactions without credentials. [S23][S24]
3. Build a Reliable Model-Backed Answer Draft
Add a provider-neutral model boundary, task instructions, validated structured outputs, and bounded failure handling to the capstone.
Make the First Model Call Behind an Adapter(20 min) Trace request, model response, and application output using a provider-neutral interface; run the fake by default and show one optional vendor-specific adapter without assuming free API access. [S32][S40][S7]
Write Task Instructions and Boundaries(20 min) Create a compact policy-answer prompt with examples, uncertainty handling, and distinct instructions and untrusted question content. [S33][S40]
Validate Structured Answers Before Use(20 min) Define a small answer contract with answer text, source references, and review status; distinguish valid JSON, schema compliance, and factual correctness. [S31][S14]
Handle Model Failures Without Retry Storms(20 min) Inject authentication, timeout, rate-limit, and malformed-output failures into the adapter and apply bounded retries with one explicit retry owner. [S32][S24]
4. Ground Answers and Integrate a Customer-Like System
Extend the same prototype with synthetic policy retrieval, evidence references, and a permission-checked local ticket API.
Prepare Synthetic Policy Documents(20 min) Inspect a small fictional policy corpus, remove irrelevant repeated content, and preserve document identifiers and access metadata. [S4][S5][S11]
Retrieve Relevant Passages with a Simple Index(20 min) Implement a scaffolded local keyword retriever and use a diagram to compare keyword, vector, and hybrid approaches without requiring a hosted vector database. [S5]
Generate Cited Answers or Abstain(20 min) Connect retrieval to the validated model boundary; keep the fake's limitations visible and review whether each answer is supported by the retrieved policies. [S5][S27][S33]
Integrate an Approved Ticket Action(20 min) Use a supplied synthetic ticket API to create a draft only after application-enforced authorization and approval of the exact action; model suggestions are never permission grants. [S14][S15][S36]
Prevent Duplicate Writes and Contract Drift(20 min) Simulate a lost response after ticket creation, reuse one operation key, reconcile the result, and test a pinned API contract before changing versions. [S16][S17][S38]
5. Measure Quality, Cost, and Safety
Build a small repeatable evaluation harness, diagnose failures, and test privacy and access boundaries before release.
Build a Small Repeatable Evaluation Set(20 min) Create human-reviewed normal, missing-evidence, ambiguous, and API-failure cases; save expected evidence and outcomes alongside each run. [S29][S27][S11]
Diagnose Retrieval and Answer Failures(20 min) Use a simple rubric and per-case review to separate missing retrieval from unsupported, irrelevant, or incomplete answers; compare a baseline and one controlled change. [S27][S29]
Measure Latency and Estimate Operating Cost(20 min) Record stage timing and request or token usage, calculate costs using explicitly supplied rates, and compare a bounded optimization against quality thresholds. [S26][S12][S8]
Test Injection, Access, and Secret-Handling Boundaries(20 min) Attempt document-borne instruction injection, unauthorized retrieval, and unapproved writes, then inspect redacted logs and document provider-data questions before any real-data use. [S33][S34][S35][S36][S37]
6. Deploy, Demonstrate, and Hand Off the Capstone
Release a local production-oriented prototype, exercise monitoring and rollback, and defend its value and limitations in a customer demonstration.
Deploy the Prototype with Validated Configuration(20 min) Use a supplied FastAPI wrapper and container scaffold to launch the completed workflow locally, with a non-container fallback; keep mock and optional live modes explicit. [S6][S7]
Observe Failures and Rehearse Rollback(20 min) Add safe request telemetry, distinguish readiness from liveness without introducing Kubernetes deployment, and use a versioned runbook to restore a known-good local configuration. [S8][S9][S13][S37]
Complete the Capstone Acceptance Demonstration(20 min) Assemble earlier artifacts into a customer-facing walkthrough covering a cited answer, an approved ticket action, abstention, a blocked action, measured results, and rollback evidence. [S11][S12][S29]
Prepare the Customer Handoff and Explain Your Decisions(20 min) Finish a concise architecture note, operator runbook, limitations register, and prioritized iteration backlog; practice explaining discovery and engineering trade-offs in an FDE interview. [S13][S20][S30][S12]