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Free tutorial · 24 lessons

Forward Deployed Engineering Tutorial

What forward deployed engineers do and how to do it well: customer discovery and scoping, integrations with customer APIs (OAuth, pagination, rate limits, webhooks), cleaning and syncing customer data, deploying into customer clouds and networks, security reviews and SSO, LLM integration, retrieval, evaluation and guardrails, and turning prototypes into production. Examples are Python you can run in the browser.

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The Role

  1. What Is a Forward Deployed Engineer? — What forward deployed engineers do, where the role came from, and how it differs from software, solutions and sales engineering.
  2. Skills and Day-to-Day Work — The technical and human skills FDEs need, how their work is organised, and what good looks like in the role.
  3. Forward Deployed Engineering: Origins & Evolution — Where the forward deployed engineer role came from, how it grew from Palantir's "Deltas" into a common role at AI and enterprise software companies, and how it compares with older customer-facing engineering roles.

Discovery and Scoping

  1. Customer Discovery — Understand the customer's workflow, data, constraints and goals before building, using structured discovery questions.
  2. Scoping and Success Metrics — Turn discovery into a scoped first deployment with measurable success criteria and an estimate of value.
  3. Deployment Plans and Milestones — Plan a customer deployment: milestones, dependencies, critical path, risks, and a status update format customers trust.
  4. Working with Stakeholders — Map stakeholders, run effective workshops and demos, handle escalations, and keep sponsors engaged.

Integration Engineering

  1. Customer APIs and Authentication — Connect to customer systems securely: API keys, OAuth 2.0 client credentials and authorization code flows, token refresh and secret storage.
  2. Pagination, Rate Limits and Retries — Read large data sets from customer APIs politely: cursor pagination, rate limits, Retry-After, exponential backoff with jitter.
  3. Webhooks — Receive events from customer systems safely: signature verification, replay protection, idempotency and fast acknowledgements.
  4. Mapping and Cleaning Customer Data — Handle real customer data: field mapping, dates and time zones, encodings, duplicates, and validation reports customers can act on.
  5. Incremental Sync and Backfills — Keep data in sync with cursors and watermarks, run backfills safely, and make loads idempotent with upserts.
  6. Testing Integrations — Test integrations without breaking customer systems: recorded fixtures, contract tests, sandboxes and synthetic monitoring.

Deploying in Customer Environments

  1. SaaS, Customer VPC, On-Premises and Air-Gapped — The ways software is deployed for enterprise customers, and what each means for networking, updates, support and security.
  2. Security Reviews, SSO and Compliance — Get through enterprise security reviews: questionnaires, SOC 2 and ISO 27001, SSO with SAML or OIDC, SCIM provisioning and data processing terms.
  3. Networks, Proxies and Firewalls — Make software work behind corporate proxies, TLS inspection, egress allow-lists and private connectivity.
  4. Observability, Support and Handover — Operate deployments you don't fully control: logs without customer data, health checks, runbooks and a clean handover to support.

AI Deployments

  1. Integrating LLMs into Customer Workflows — Put a large language model into a real workflow: structured outputs, validation, fallbacks, human review and cost control.
  2. Retrieval-Augmented Prototypes — Build a retrieval-augmented generation prototype on customer documents: chunking, ranking, citations and freshness.
  3. Evaluating AI Systems — Measure AI quality with a golden test set built from real customer cases, track it in CI, and decide release thresholds with the customer.
  4. Guardrails and Human Review — Keep AI deployments safe: input and output checks, permission-aware retrieval, confidence thresholds, audit trails and human approval.

Delivering Value

  1. From Prototype to Production — Turn a convincing prototype into a supported production deployment: configuration, reliability, security, monitoring and ownership.
  2. Feeding Field Learnings Back to Product — Turn customer work into product improvements: patterns, evidence, prioritised requests and upstreamed code.
  3. Next Steps — Where to go next: the Forward Deployed Engineering course, AI and integration courses, cloud and networking tutorials, and interview practice.

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