Employed from Day One
Join Red Alpha as a full-time employee and receive compensation throughout training
Your future in AI starts here. Become a Forward Deployed Engineer.
Get hired by Red Alpha, build the technical and client-facing skills to deploy AI in real enterprise environments, then put those skills to work with our clients
Paid from Day OneFull-time Red Alpha employee
Intensive AI EngineeringHands-on, deployment-focused training
Real Career PathwayTrain towards enterprise deployment
Highly SelectiveBuilt for experienced technical talent
Train for the realities of enterprise AI deployment
Red Alpha FDE ProgramBuilt by Red Alpha. Trusted by leading organisations
Red Alpha selects, trains and deploys technology professionals into some of the world's most demanding enterprise environments
What is a Forward Deployed Engineer?
An FDE sits at the intersection of:
Software Engineering + AI + Problem Solving + Customer Delivery
An FDE doesn't just build AI in isolation. They work closely with organisations to understand complex problems, design solutions, integrate AI into existing systems and help take those solutions from idea to production.
Understand
Translate complex business problems into technical requirements
Build
Develop AI applications, integrations and workflows
Deploy
Bring solutions into real enterprise environments
Own
Work with stakeholders from discovery through delivery
Why FDE is becoming one of AI's most important roles
Companies have spent the last few years experimenting with AI. The next challenge is making it work inside real organisations. That requires engineers who can understand business problems, build technical solutions and deploy them into complex enterprise environments. This is where FDEs come in.
As AI moves from experimentation to implementation, demand is growing for engineers who can bridge technology, customers and real-world deployment.
FINANCIAL TIMESSep 2025
800%
Growth in FDE job postings
Forward Deployed Engineer job postings rose sharply as companies increased investment in taking AI into production.
Jan to Sep 2025Read article →
ForbesMay 2026
AI's challenge is no longer just building the model
Many enterprise AI initiatives struggle when it comes to integration, deployment and adoption. FDEs are designed to close that gap.
95% of pilots stallRead article →
Salesforce2026
Today's hottest role: forward deployed engineer
Salesforce announced plans to build a major FDE organisation focused on helping customers turn AI into working enterprise solutions.
1,000 FDEsRead article →
Everything is built around what you become.
You are not joining another course. You are joining a selective employment pathway designed to make you useful in real AI deployment environments.
Engineer with AI depth
Go beyond AI tools and learn how AI systems are actually built and deployed
Enterprise-ready
Learn how to work within complex infrastructure, security and business environments
Customer-facing
Build the communication and problem-solving skills needed to work directly with stakeholders
Deployment-ready
Develop the technical depth to take AI beyond proof-of-concept
Two pathways. One shared FDE journey
FIE starts first with 4 weeks of engineering foundations. FDE candidates then join the cohort for the shared 10-week programme.
1
Week 1 - 4Engineering FoundationsProduction-ready Python, software engineering fundamentals, Git, testing, APIs, networking, cloud and enterprise systems. (FIE starts here)
2
Weeks 5 – 6Applied AI FoundationsLLMs, prompting, model APIs, embeddings, RAG and building AI-powered applications (FDE starts here)
3
Weeks 7 – 8AI Engineering & IntegrationAgents, tool use, MCP, APIs, enterprise data and integrating AI into existing systems
4
Week 9 - 10Deployment & InfrastructureDocker, cloud, Kubernetes, infrastructure, CI/CD and taking applications into production
5
Week 11 - 12Production AIObservability, evaluation, monitoring, reliability, AI security and operating AI systems responsibly
6
Week 13 - 14Forward DeploymentDiscovery, solution design, stakeholder communication, customer delivery, capstone and handover
Choose the path that matches your experience.
Both tracks lead toward customer-facing AI deployment work. Your experience determines where you begin and how much ownership you take during the cohort.
Forward Integration Engineer
2 to 3Years required
Apply to this track if you
- ✓Have two to three years of professional engineering experience
- ✓Work in software, backend, cloud, data, ML, DevOps, or support engineering
- ✓Are comfortable in Python and have worked in a production codebase
- ✓Want AI to be the job rather than something you read about on weekends
In the cohort you will
Build under the guidance of engineers who have shipped AI in production, own real components of the capstone, and present your own work at demo day.
Forward Deployed Engineer
5+Years required
Apply to this track if you
- ✓Have five or more years of professional engineering experience
- ✓Have owned a complex technical project from start to delivery
- ✓Work as a senior or staff engineer, architect, consultant, or engineering manager
- ✓Can hold a technical conversation with a customer without a manager in the room
In the cohort you will
Lead technical workstreams, own customer facing conversations, shape solution design, and mentor associates rather than repeat fundamentals you already have.
Not sure which pathway fits you? Apply to the FDE Programme and our selection team will assess the appropriate entry point based on your experience
Both tracks require
Technical background
Selected for potential. Trained for impact. Deployed where it matters
01
Select
We assess your technical foundation, problem-solving ability and potential to succeed in demanding enterprise environments
02
Train
Join Red Alpha as a full-time employee and undergo intensive FIE or FDE training designed around real-world AI engineering and delivery
03
Deploy
After successfully completing the programme, move into client-facing enterprise assignments matched to your skills, experience and deployment requirements
Weeks 1 to 4: Foundations
Engineering
- ✓Python for production, not scripts
- ✓Testing, linting, and Git practice
- ✓SDLC, secrets, and CI/CD basics
Enterprise environment
- ✓Network architecture and the OSI model
- ✓DNS, firewalls, ACLs, NAT, and routing
- ✓Layer 1 to 3 troubleshooting
Weeks 5 to 7: Services and infrastructure
Build
- ✓REST APIs with Python and Flask
- ✓HTTP, JSON responses, and Postman
- ✓Exposing AI as a service
Deploy
- ✓AWS EC2, S3, IAM, Lambda, and VPC
- ✓Docker images, Compose, and Kubernetes
- ✓Terraform and Ansible
Weeks 8 to 11: Applied AI
AI development
- ✓Data analysis and preparation
- ✓LLM APIs, Hugging Face, and local models
- ✓RAG, tools, memory, and evaluation
Tooling and serving
- ✓MCP and tool or context integration
- ✓AI assisted development and prompt work
- ✓vLLM, Triton, SageMaker, and NIM
Weeks 12 to 14: Production and capstone
Run it
- ✓Monitoring, observability, and AI security
- ✓Splunk, SPL, dashboards, and alerting
- ✓Correlation searches and SIEM use cases
Hand it over
- ✓Deploy a production LLM application
- ✓Architecture diagram and runbook
- ✓Final presentation and handoff
The stack you will learn to use.
Build practical experience across the languages, cloud platforms, AI tools, deployment systems, and security technologies used in real enterprise environments.
Language and codeCloud and infrastructureContainers and deliveryAI developmentAgents and toolingServing and deploymentMonitoring and security
01 / Language and code
PythonFlaskREST APIsGitTesting and lintingSQL
Covered in Weeks 1 to 5
02 / Cloud and infrastructure
AWS EC2S3IAMLambdaVPCTerraformAnsible
Covered in Weeks 6 to 7
03 / Containers and delivery
DockerDocker ComposeKubernetesCI/CDSecrets management
Covered in Weeks 3 to 7
04 / AI development
LLM APIsHugging FaceOllamaRAGFine tuningGradio
Covered in Weeks 8 to 10
05 / Agents and tooling
MCPAgentic workflowsPrompt engineeringAI assisted IDEsLLM as judge
Covered in Weeks 9 to 10
06 / Serving and deployment
vLLMNVIDIA TritonAWS SageMakerNVIDIA NIMModel serialization
Covered in Week 11
07 / Monitoring and security
SplunkSPL and dashboardsSIEM use casesObservabilityAI guardrails
Covered in Weeks 12 to 13
From Engineer to Forward Deployed Engineer
Build on the engineering experience you already have and develop the AI, deployment and customer-facing skills needed to move into one of tech’s emerging roles.
Today
Software Engineer
Backend Engineer
Data Engineer
Cloud / DevOps Engineer
AI / ML Engineer
Solutions Engineer
Next Step
Forward Integration Engineer (FIE)
For engineers with around 2–3 years of experience who need a stronger foundation before progressing into forward deployment
Forward Deployed Engineer (FDE)
For experienced engineers with 5+ years who are ready to combine engineering, AI, solution design and customer delivery
Where it can take you
Senior FDE
AI Solutions Engineer
Solutions Architect
Technical Delivery Lead
AI Engineering Lead
Principal / Lead FDE
As you gain deeper technical expertise, deployment experience and customer ownership, your career can progress into senior engineering, architecture and technical leadership roles.
We invest in your career from day one.
Training is only one part of the experience. You join Red Alpha as an employee and receive the structure, support, and benefits needed to focus on becoming deployment-ready.
Fully Sponsored Training
Red Alpha covers the full cost of your intensive training and development
Employee Benefits
Receive the applicable Red Alpha employee benefits during your employment
Expert mentorship
Learn from practitioners across AI, cloud, software, cybersecurity, and enterprise delivery.
Career Development
Strengthen your technical delivery, communication, problem-solving and stakeholder skills
Long-term community
Continue learning through Red Alpha mentors, peers, customer projects, and alumni connections.
Questions worth asking.
What is a Forward Deployed Engineer?+
An FDE works directly with customers to understand complex problems, build technical solutions, integrate them into existing systems, and support deployment and adoption.
Is this a traditional training course?+
No. It is an employment and career development pathway. You are hired first, then trained through intensive practical work in applied AI engineering and enterprise delivery.
Do I need previous AI experience?+
Requirements depend on the track. You should have a relevant technical foundation, strong problem solving ability, and the capacity to learn quickly. The FDE Handbook covers eligibility in full.
Is the program paid?+
Yes. You are a paid Red Alpha employee for all 10/14 weeks of training, not a student. Compensation and benefits are shared with qualified applicants during the process.
What happens after the program?+
Graduates work on Red Alpha initiatives or customer projects, matched on skills, experience, performance, location, and business need.
Is there an employment commitment?+
Yes. Red Alpha pays your salary and the full cost of training, so we ask for a minimum service period in return. You will see the exact length, the terms, and the route to permanent conversion or direct employment in writing before you accept anything. Nothing about it is a surprise later.
Can experienced engineers apply?+
Yes. The experienced track is built for established engineers moving into AI deployment, technical leadership, and customer facing delivery.
Build AI that moves beyond the demo
Turn your engineering experience into a career deploying AI where it matters