Production AI FDE jobs

Production AI FDE jobs are about making an AI product work for a real customer after the demo. The work can include architecture, application code, data and identity boundaries, evaluation, deployment, security, and operational follow-through. A title may say “forward deployed engineer,” “customer engineer,” or “applied AI”; read the responsibilities to see whether the role owns production outcomes.

What the work includes

The first responsibility is turning a customer problem into a technical plan that can survive real constraints. The Google AI Outcome Customer Engineer posting describes assessing feasibility, designing enterprise AI integrations across data pipelines, identity, connectors, and compliance boundaries, and diagnosing implementation issues at code level. That combination is different from presenting a product or advising a customer without owning the implementation.

Production AI work continues after the first successful request. An Amazon security FDE posting includes security and architecture reviews, threat modeling, penetration testing, CI/CD gates, scanning tools, and controls for prompt injection, data isolation, output filtering, and endpoint hardening. An EY senior manager posting similarly calls out evaluation frameworks, observability, resilience, security, responsible AI, and LLM operations. These details show what “production” means: the system needs tests, boundaries, monitoring, and a plan for failure.

Some roles remain close to the customer environment. The Veeam posting in the jobs catalog describes taking deployments from implementation through scaled production use, deploying across public cloud, private cloud, and on-premises systems, and building integrations, data pipelines, classifiers, automations, and agent workflows. The exact stack changes by employer. The durable skill is being able to find the boundary between the product, the customer’s data, and the customer’s operating process, then make that boundary work.

How to read a production AI listing

Look for four kinds of ownership:

• Build: Does the job write production code, or only prototypes and demos?
• Integrate: Does it connect identity, APIs, data stores, and customer systems?
• Prove: Does it define evaluations, tests, or acceptance criteria for the AI behavior?
• Operate: Does it own deployment, observability, incident response, security, or handoff?

Questions to ask before applying

Ask which stage the FDE owns: evaluation, proof of concept, implementation, production launch, or ongoing operations. Ask who operates the system after launch and how field feedback reaches product and engineering. Ask how the team measures quality, latency, cost, security, and customer adoption. If the listing names travel, cloud environments, or customer data, ask what those terms mean for the specific account and location. Silence in a listing is a question, not evidence that the responsibility is absent.

The linked results use the production-engineering filter with an LLM keyword. They show jobs that mention LLMs in that work category, alongside other production responsibilities. Browse production-engineering jobs mentioning LLMs, compare the research definitions, and review FDE skills and learning. For compensation, use the FDE salary data.