Although enterprises have access to increasingly capable AI models, many still lack the people needed to turn them into reliable production systems
The resulting gap has created demand for “forward-deployed engineers” (FDEs), a hybrid technical and business role designed to move AI from a promising demonstration into workflows that produce measurable results.
A recent Christian & Timbers study estimates roughly 17,000 forward-deployed engineers work in the United States, but only about 2,000 have repeatedly delivered “significant” enterprise value.
The executive search firm defines that “elite” tier as engineers who have completed multiple enterprise AI deployments and generated at least $10 million in revenue or savings.
Christian & Timbers clarifies the estimate is not a federal labor count, adding the title “forward deployed engineer” remains fluid, but it hints at a broader problem for organizations —buying AI is easier than integrating it into productive workflows.
More Than an AI Engineer
These types of engineers typically embed with a business or customer team and take responsibility for a deployment from problem discovery through production. The work combines software engineering, model evaluation and integration with process redesign, stakeholder management and organizational change.
“Forward-deployed engineers are taking what used to be a very technical skillset and marrying that with business transformation to work cross-functionally,” says JC Christian, president of Christian & Timbers. “They have experience solving systems problems, people problems, and process and technology integrations.”
That breadth separates the role from one focused primarily on model development. An FDE must understand how employees perform a task, identify where AI can improve the workflow and determine whether the resulting system delivers enough value to justify its cost and risk.
Elite FDEs add a record of execution, able to identify pain points, define success with management, coordinate domain experts, lead development and report financial outcomes to executives.
Build a Two-Sided Skillset
For IT professionals, the clearest technical preparation includes experience with orchestration layers, model evaluation and end-to-end large language model integration. Production work also requires conventional engineering disciplines such as testing, security, observability, data integration and reliability.
Those capabilities are only half the profile, as FDEs must communicate with executives and users, map processes, earn trust and translate technical trade-offs into business terms.
Christian says he recommends strengthening writing, public speaking and interpersonal skills alongside technical expertise.
“Without the technical skills, professionals will operate more like consultants from years past, and without the business transformation piece, professionals will struggle to translate their technical expertise into business outcomes,” he explains.
Software engineers with an aptitude for managing stakeholders have one of the most direct paths into the field. Candidates from adjacent roles must still show that they can build production systems rather than stop at recommendations or prototypes.
Find Production Experience
The shortage creates a familiar hiring paradox: Employers want candidates with successful deployments, but professionals cannot develop that record without an opportunity to work on one.
Christian argues that aspiring FDEs should seek roles where they can work under an experienced deployment leader. An elite FDE or vice president of AI can help less-experienced engineers learn how technical decisions, process improvements and business outcomes fit together at enterprise scale.
“By working with those elite FDEs and AI leaders, professionals will open themselves to the possibility of earning that elite status in the process,” he says.
That makes mentorship and team structure important questions during a job search. Candidates should ask who owns production AI deployments, how outcomes are measured and whether they will participate in discovery, implementation and post-launch improvement.
Meanwhile, internal rotations connecting software teams with operations, finance, customer service or other business functions can provide similar exposure.
Prove Impact Without the Title
Professionals do not need to hold the FDE title before presenting themselves as candidates. A résumé should document AI systems placed into real workflows and quantify the resulting savings, revenue, cycle-time reduction, quality improvements or risk avoidance.
“Any professional that has deployed industry-specific agentic workflows to save their company money should be including this on their résumé,” Christian says.
Strong examples should explain the original business problem, the candidate’s technical contribution, the teams involved and the measured result. Experience with orchestration, evaluation and LLM integration matters, but so does evidence that employees adopted the system and the workflow continued operating after launch.
Candidates should avoid presenting the number of models tested or prototypes built as a substitute for impact. Employers seeking FDEs are looking for evidence that an engineer can connect technical execution to an operational or financial outcome.
A Midcareer Opening
In the short term, organizations are likely to compete most aggressively for the small group with proven large-scale results. Over time, however, those senior hires will need to build teams and train engineers from adjacent roles, creating opportunities for midcareer IT professionals.
The opportunity comes with unusually high expectations. Businesses want immediate returns, while transitioning candidates may still be learning how to manage complex deployments, executives and business risk simultaneously.
“This will create a mismatch that may prove lucrative for mid-career professionals but also may stretch them beyond their current capabilities,” Christian says.
