Certified Forward Deployed Engineer Mastery

Master python for applied systems by building a working repository with tests, apis, and data handling, while validating risks, trade-offs, and evidence for customer rollout.

Use git, GitHub, and collaboration workflows to make code changes reviewable and reversible, delivering a testable, production-ready repository with tests, clear ownership, validation, and outcome focus.

Master structured query language and data handling to transform a prototype into a production candidate, building maintainable repositories with tests and APIs to manage customer data and validate risks.

Apply system design principles to build scalable, reliable, and understandable systems by creating architecture sketches and interface contracts, identifying risks and trade-offs, and validating with evidence for customer-ready delivery.

Master modular application design by dividing software into testable, replaceable parts, creating architecture sketches and interface contracts, and validating decisions with evidence to deliver clear customer outcomes.

Define clear service boundaries and interfaces to enable seamless handoffs and rapid progress. Create architecture sketches and interface contracts, validate high-risk assumptions with evidence, and document outputs for stakeholder understanding.

Learn to design resilient systems with state, events, and messaging, creating architecture sketches and interface contracts, validating with evidence and clear handoffs before customer rollout.

Master cloud concepts for forward deployed engineers by turning laptop demos into managed cloud delivery, using deployment checklists and operations runbooks to manage risks and pilots.

Implement ci/cd pipelines to automate build, test, security, and deployment, ensuring repeatable releases, customer outcomes, and evidence-driven decisions through deployment checklists and runbooks.

Develop expertise in vector databases and search for semantic retrieval using embeddings, indexes, filters, and ranking. Create a data inventory, models, and an access plan with validated risks.

Master prompt engineering basics by structuring instructions, constraints, and context to improve model reliability, map inputs and risks, validate with evidence, and deliver AI design nodes aligned with business outcomes.

Design agent architecture fundamentals, including loops, goals, tools, memory, planning, and stopping conditions, for safe artificial intelligence automation with evidence-backed validation and logs.

Connect model reasoning to controlled actions using type tools and validated parameters, enabling tool use, agent workflow design, risk management, and evidence-based customer outcomes.

Master workflow orchestration to turn ai steps, tools, and human input into structured agent workflows, with an agent design and evaluation checklist, risk assessment, and evidence-based validation for customer outcomes.

Apply agent evaluation and guardrails to ensure task completion within bounded behavior, using tools and memory of context, and an evaluation checklist to prove progress for customer rollout.

Explain how internal knowledge systems enable AI product teams to access organizational knowledge, validate risks and trade-offs with evidence, and deliver a field-ready product workflow prototype and user experience review.

Explore AI product user experience patterns—transparency, correction, confidence, and user control—tied to workflows. Prototype workflows and user experience reviews, identify risks and trade-offs, and craft a field-ready artifact with validation.

Identity and access management links users, groups, roles, authentication, and authorization to artificial intelligence application behavior, enabling integration maps, deployment constraint logs, and clear validation before customer rollout.

Learn to design integration maps and deployment constraint logs for enterprise systems, validate high-risk assumptions with evidence, and articulate customer outcomes with clear owners, constraints, and acceptance checks.

Learn how CRM, ERP, and ticketing integrations connect AI features to enterprise systems, create integration maps, and validate progress with inputs, owners, constraints, and metrics.

Learn to design webhooks and event-driven flows that enable near-real-time responses, create integration maps and deployment constraint logs, and validate high-risk assumptions with evidence for customer-ready delivery.

Apply security principles for AI delivery, including least privilege, defense in depth, secure defaults, and threat modeling, to craft security review packets and governance plans with clear evidence.

Policy enforcement and approval flows create security review packets and governance plans, map inputs and owners, assess risks, validate with evidence, and enforce least privilege to protect data and compliance.

Explain responsible ai practices that balance usefulness with fairness, transparency, accountability, and trust in secure ai delivery. Create security review packets and governance plans with risks and a validation path.