Forward Deployed Engineer – Professional [CFDE-P]
For enterprise environments. Certifies an FDE who can operate inside complex organisations with legacy infrastructure, compliance obligations, and multi-stakeholder politics. 16 modules across 18 weeks, full-time, instructor-led.
Level
Professional (CFDE-P)
Duration
18 weeks, full-time
Structure
15 modules + 3-week capstone
Format
Online, instructor-led
Course
Open to all (Foundation knowledge assumed)
Exam prereq
4+ years relevant experience + CFDE-F or challenge exam
Tools expected
Python, Git, Docker, cloud platforms, enterprise systems
Register Interest →What CFDE-P certifies
Professional certifies an FDE who can operate inside complex enterprise environments. The kind where getting production credentials from the security team takes longer than building the prototype. Where the CTO wants speed, the CISO wants security, and the CFO wants cost reduction. Where the data sits in SAP, the identity layer runs on SAML, and the deployment target is an air-gapped network with no internet access.
Foundation covers everything an FDE needs with standard tools, cloud APIs, and direct client relationships. Professional adds the layer that only exists inside large, complex organisations: legacy system integration, enterprise identity, compliance frameworks, multi-stakeholder navigation, production observability at enterprise scale, and the political and organisational skills to get things done in environments where resistance is the default response.
The capstone simulates a full enterprise engagement: legacy system constraints, compliance requirements, multiple stakeholders with conflicting priorities, ambiguous requirements, and a tight deadline. Three weeks to build, followed by a 45-minute oral defence with a practitioner panel.
Who this course is for
The CFDE-P course is open to anyone, but assumes Foundation-level knowledge. We recommend it for engineers targeting enterprise FDE roles at organisations deploying AI across regulated or complex environments, and for engineers already working in enterprise settings who want to formalise their deployment practice.
The certification exam (separate from the course) requires 4+ years of relevant experience in software engineering, ML engineering, data engineering, DevOps, solutions architecture, or a closely related discipline, plus either CFDE-F completion or a passing score on the challenge exam.
Course syllabus
16 modules. The first 15 run one per week. Module 16 (capstone) runs over three weeks. Each module addresses a capability that exists only in enterprise environments.
Module 1Enterprise AI Planning+
Pre-project strategy. Business case development, roadmap creation, resource allocation. Securing early sponsorship for the AI initiative. Planning answers “how do we get the green light.” Strategic sponsorship and initial stakeholder alignment.
Module 2Enterprise AI Integration+
Connecting AI systems to existing enterprise infrastructure. Legacy databases, ERPs (SAP, Oracle), CRMs, mainframes, undocumented APIs. Working within constraints you did not design and cannot change. Data migration, ETL pipelines, format transformation. The hands-on module for connecting AI to what already exists.
Module 3Identity Management and Access Control+
The practical reality of getting production credentials from an enterprise security team. SSO, SAML, OIDC implementation. Zero-trust architectures. Identity federation across systems. This is the skill that separates enterprise FDEs from startup FDEs.
Module 4Restricted and Air-Gapped Environment Deployments+
Deploying AI systems inside client-controlled infrastructure with restricted or no internet access. Government, defence, and financial services environments. Private cloud, on-premise GPU clusters, air-gapped networks. Packaging models and dependencies for offline deployment. Testing without external API calls. Includes a hands-on lab session informed by Sovereign AI Lab (SAIL) research.
Module 5Enterprise AI Security and Compliance+
Technical compliance: applying controls, hardening. Adversarial prompt testing, data poisoning detection, prompt injection defence. AI supply chain security: model and dataset provenance tracking, secure model registries, third-party model risk assessment, open-source model vetting.
Module 6AI Risk Management, Safety, and Ethics+
Understanding what can go wrong with AI systems and how to prevent, detect, and respond to it. Incident response. Model risk, reputational risk, vendor risk. Bias and fairness: audit methodology, regulatory expectations, what biases to look for. Multi-agent emergent behaviour. Safety engineering.
Module 7AI Economics and FinOps+
The financial discipline required to keep enterprise AI solvent. Per-token billing, GPU scarcity, volatile pricing. Token and GPU cost modelling. Chargeback and showback. Cost baselining, GPU optimisation, quota enforcement, cross-team chargeback. Business case for AI investment. Without this, enterprises suffer agent sprawl and surprise inference bills that freeze further AI investment.
Module 8LLM Evaluation and AI Observability at Scale+
Logging, alerting, and monitoring for systems serving thousands or millions of users. Token usage tracking and cost attribution across business units. Output drift detection. SLA compliance monitoring. Latency monitoring. Tools: LangSmith, Langfuse, Arize Phoenix, Datadog. Building observability that the client’s operations team can own after you leave.
Module 9Agent Lifecycle Management+
Not building agents (that is Foundation Module 10). Managing the full lifecycle: agent versioning, deprecation, retirement. Permission scoping. Staged rollouts from supervised to semi-autonomous to fully autonomous. Agentic workflows need their own lifecycle beyond traditional MLOps.
Module 10AI Governance and Regulatory Compliance+
Building governance structures for the client: who approves model changes, who audits, who has override authority. Governance-as-code, kill switches. EU AI Act, NIST AI RMF, UK AI Safety Institute. Audit trails. Incident reporting. Documentation requirements for regulated deployments. Preparing for regulatory inspections. Operational governance and regulatory navigation.
Module 11Enterprise AI Quality Assurance+
Systematic quality engineering between observability (which monitors) and governance (which enforces policy). Model validation, quality gates, fairness testing, client acceptance criteria. Test strategy, validation datasets, performance thresholds, QA processes across the AI lifecycle.
Module 12Enterprise AI Project Management+
Execution mechanics. Timelines, milestones, dependency management. Sprint cadence. Status reporting. Commercial awareness: AI projects are business instruments, not just technical deliveries. Different from Module 13 (Adoption) which covers the human operating system.
Module 13Enterprise AI Adoption and Change Management+
The human operating system. Ground-level resistance, fear, coalition building, AI literacy, cultural shift. Coalition building, AI literacy programme design (EU AI Act mandates AI literacy as a legal compliance requirement). Overcoming workforce resistance. Adoption metrics. Ongoing transformation during and post-deployment.
Module 14Enterprise AI Requirements Engineering+
How to elicit, document, and translate messy business needs into technical specifications. Requirements elicitation from resistant stakeholders. Specification and validation. An established discipline (IEEE, IREB certify it) applied specifically to AI deployments in enterprise environments.
Module 15Production Telemetry and Platform Evolution+
The mechanism by which the enterprise learns from live AI and hardens it. Product feedback loops: field patterns to platform features. Identifying repeatable deployment patterns. Codifying field insights into internal documentation. Contributing to product roadmap from the field. Repeatable deployment patterns and internal playbooks. The skill that makes an FDE more valuable than a consultant.
Module 16Enterprise AI Capstone and Board Defence (3 weeks)+
Full enterprise engagement simulation running over three weeks. Realistic brief with legacy system constraints (mock SAP/Oracle environment), compliance requirements (SOC 2 evidence, data residency), multiple stakeholders with conflicting priorities (CTO wants speed, CISO wants security, CFO wants cost reduction), ambiguous requirements, and a tight deadline. Deliverables: discovery documentation, architecture design, working prototype integrated with mock enterprise systems, compliance documentation, deployment plan, executive presentation, and product feedback report. Assessed by practitioner panel via 45-minute oral defence. Graded on: technical quality, enterprise-readiness, stakeholder management, communication under pressure, and commercial awareness.
CFDE-P FAQs
Who is the Professional course for?+
Engineers targeting enterprise FDE roles or engineers already working in enterprise environments who want to formalise their deployment practice. The course is open to anyone but assumes Foundation-level knowledge. The certification exam requires 4+ years relevant experience plus CFDE-F or the challenge exam.
What will I be able to do after completing Professional?+
Operate as an FDE inside complex enterprise environments. Integrate AI with legacy systems (SAP, Oracle, mainframes). Navigate enterprise identity and zero-trust architectures. Deploy in air-gapped and restricted environments. Build governance structures, manage compliance across SOC 2, HIPAA, GDPR, and FedRAMP. Communicate at board level. Manage multi-stakeholder politics.
What is the time commitment?+
18 weeks, full-time, instructor-led. 15 weekly modules followed by a 3-week enterprise capstone with oral defence.
Can I skip Foundation and go straight to Professional?+
The Professional course is open to anyone, but assumes Foundation-level knowledge. The Professional certification exam requires either CFDE-F completion or a passing score on the challenge exam. The challenge exam assesses Foundation-level competencies.
What is the SAIL lab session in Module 4?+
Module 4 covers air-gapped and restricted environment deployments. Sovereign AI Lab (SAIL), AIU’s research arm, provides hands-on lab content for deploying AI systems on infrastructure with no internet access. This draws on SAIL’s published research on privacy-preserving architecture and small-model deployment.
How does Professional differ from Foundation?+
Foundation certifies a complete FDE for startups and SMEs. Professional adds the enterprise layer: legacy system integration, identity management, air-gapped deployments, compliance frameworks, AI economics, governance, multi-stakeholder navigation, and board-level communication. Both run 18 weeks. They are separate scopes of practice, not junior and senior versions of the same thing.
Does completing the course give me the CFDE credential?+
No. The course and the certification exam are separate products. The course teaches the skills. The exam certifies them. You must pass the standardised CFDE certification exam to earn the credential at the Professional level.
Can my employer fund this?+
Yes. AIU issues invoices suitable for employer L&D budgets and professional development funds. For group enrolments, enterprise pricing, or on-premises delivery, contact hi@aiu.ac.
What if I don’t pass the exam?+
Candidates who do not pass may retake the exam. Retake policy details will be communicated to all registered candidates before the examination period opens.
What is the capstone like?+
A full enterprise engagement simulation over three weeks. You receive a brief with legacy system constraints, compliance requirements, conflicting stakeholder priorities, and a deadline. You deliver architecture, working prototype, compliance documentation, deployment plan, and an executive presentation. Assessed by a practitioner panel via a 45-minute oral defence.
Register interest for CFDE Professional
The programme is being prepared for global online delivery. Leave your details to be notified when enrolment opens.
Artificial Intelligence University · UKPRN 10095512 · Artificial Intelligence Uni Ltd · Company #14543918
