Red Hat OpenShift Virtualization is Red Hat’s approach to running virtual machines (VMs) and containers on a single Kubernetes platform, allowing organizations to modernize traditional virtualization environments without maintaining separate VM and container infrastructures.

OpenShift Virtualization is not simply a VMware replacement;

It is a platform modernization strategy:

1- It allows organizations to run traditional VMs and modern containerized applications on a single Kubernetes platform

2- Reducing infrastructure silos

3- Enabling automation

4- Supporting cloud-native transformation

5- Providing a migration path away from traditional hypervisor-centric architectures. For enterprises building a future-state digital platform,

It can serve as a bridge between legacy systems and cloud-native services.

A Forward Deployed Engineer (FDE) is a hybrid technical role that embeds directly within a client’s environment. Instead of building software strictly from a remote headquarters, FDEs work closely with customers to prototype, integrate, and deploy custom solutions—acting as the bridge between technical capability and operational reality.

The FDE model originally gained massive prominence at companies like Palantir and has exploded in the AI/ML sector (where experts like OpenAI deploy FDEs to roll out frontier models into production).

The FDE Lifecycle

Discovery & Scoping: Working on-site to analyze the client’s complex workflows and infrastructure constraints.

Rapid Prototyping: Iterating quickly in close collaboration with the users to build hackathon-style proofs of concept.

Production & Rollout: Hardening and integrating the software (or AI pipeline) directly into the customer’s enterprise environment.

Core Skillset

An ideal FDE requires a unique, rare overlap of skills:

Elite Software/ML Engineering: Strong full-stack or data engineering chops to build and scale functional code.

Customer Empathy & Product Sense: The ability to translate vague business needs into hard technical requirements.

Problem Solving: Navigating complex enterprise politics, compliance, and legacy infrastructure.

Career Trajectory & Compensation

Given the immense push for AI integration and custom enterprise software, the demand for this role has skyrocketed, with roles often commanding base salaries well over \(\$120,000\) to \(\$180,000\) (and often much higher in top-tier tech/AI hubs). For more insights into this career track, you can read the a16z FDE Fellowship or explore Invisible Tech’s FDE Guide

But you don’t become an FDE by reading a job description. You become one by thinking like one.

1. Start With Context, Not Code

Traditional engineering starts with a requirement: “Build feature X by date Y.”

FDE thinking starts earlier: “What problem is the user trying to solve, and why?”

Before touching code, an FDE spends time understanding:

• who the user is
• what slows them down
• what they tried in the past
• what success looks like
• what they are actually afraid of

This isn’t project intake. It’s human-centered diagnosis. We used to do all of the above using design thinking practices (user empathy map for example)

Practical example: Meeting action items chaos

A support manager tells you: “We waste too much time turning meeting discussions into clear actions. Everyone forgets what was assigned to whom.”

This is not a requirement. It’s a pain point.

You ask the deeper questions:

• How many meetings per week?
• Where should actions live?
• What’s the fastest way to improve life for the team?

She says: “If Slack could automatically show decisions and clear action items, that would save hours.”

Great. Now the real FDE work begins.

2. Treat Ambiguity as a Feature, Not a Bug

FDEs rarely get neat inputs. They get half-ideas:

“We want something that… summarizes meetings? I think?”

Instead of freezing, FDEs build clarity through small prototypes.

3. Solve for Outcomes, Not Artifacts

A traditional engineer might propose:

• A multi-page UI
• A structured meeting template
• A robust workflow engine

An FDE asks: “How quickly can I deliver meaningful time savings?”

If the solution saves 20 hours/week across managers, it’s worth more than any polished interface.

Impact first. Everything else second.

4. Build in the Customer’s Environment, Not in Isolation

FDEs don’t build in notebooks or local sandboxes. They build where the users already live.

5. Think in Systems, Not Features

Small features always have ripple effects. FDEs anticipate these early.

When this Slack bot goes live:

• Will managers want to push actions into Jira?
• Will summaries need to sync with Confluence?
• Will audit logs be required?
• Will access controls matter later?

You think about the ecosystem, not the endpoint.

6. Deliver Fast, Learn Fast, Iterate Fast

You demo the prototype. Manager responds:

• “Can we also capture blockers?”
• “Can action items push to Jira automatically?”
• “Can we add a sentiment score to detect heated discussions?”

Every comment becomes a controlled iteration.

FDE success comes from:

• fast deployment
• fast learning
• fast refinement

This is the opposite of waterfall. It’s closer to scientific method.

7. Communicate With Clarity

After each iteration, FDEs write:

• a summary of decisions
• the architecture
• integration notes
• trade-offs
• test results

Writing keeps everyone aligned. It also prevents “hero dependency” where only you know how the system works.

8. Build Trust Faster Than You Build Technology

You didn’t just deliver software. You delivered trust, and trust is what gives FDEs influence.

Where This Mindset Takes You

Once you start thinking like an FDE, your entire approach to engineering changes. You stop waiting for perfect requirements. You start looking for friction. You learn to design for impact, not just completion. And you build systems that actually survive inside real enterprise environments.

This mindset opens doors to:

• AI product leadership
• applied ML engineering
• agentic system design
• complex integration work
• customer-facing engineering roles
• startup-style problem solving inside big companies

This is the essence of Forward Deployment Engineering. As AI moves from prototypes to production, this mindset becomes the difference between teams that make progress and teams that make impact. I’d love to hear how you imagine this role shaping your own organization’s future.

ITIL & ITSM Excel Templates Suite: Service management maturity is built in layers.

Modern ITSM is no longer just ticket handling. It connects operational foundations, support execution, change delivery, employee experience, governance, automation, and measurable business value.

When these layers work together, IT teams move from reactive service support to structured service performance.v

Modern AI is powered by an entire ecosystem of processors:

CPU → Coordinates everything

GPU → Trains massive models

TPU → Accelerates tensor operations

NPU → Brings AI to phones & laptops

LPU → Delivers ultra-fast LLM responses

DPU → Handles networking, security & data movement

Depending on ALL of them working together.

A trillion-parameter model is useless if:

• Data can’t reach it fast enough

• Inference is too expensive

• Edge devices can’t run it

• Infrastructure can’t scale

The best AI does not mean ONLY best model, but also best compute stack.

Models get the headlines. Chips run the world.